Device and method for performing 2-1 EDP protocol in quantum communication system

Abstract

The present disclosure provides a device and method capable of performing a 2-1 EDP protocol in a quantum communication system. The present disclosure provides a device and method capable of performing a 2-1 EDP protocol in a quantum communication system, which can increase fidelity efficiency or increase success probability performance compared to QPA or recurrence protocols by presenting a transformation operator of a Bell diagonal state.

Description

Device and method for performing 2-1 EDP protocol in quantum communication system

[0001] The present disclosure relates to a device and method for performing a 2-1 EDP protocol in a quantum communication system. The present disclosure relates to a device and method for performing a 2-1 EDP protocol, which can increase fidelity efficiency or success probability performance compared to QPA or recurrence protocols by presenting a transformation operator for a Bell diagonal state.

[0002]

[0003] There are existing 2-1 EDP protocols, QPA protocols, and Recurrence protocols. Depending on the input state, the QPA protocol may have lower fidelity of the output state than the input state, and the Recurrence protocol may not be able to sufficiently increase fidelity or have a low success rate. Alternatively, both performances are lower than QPA when performing repeated rounds. The input state can be generated by selecting the Keep syndrome in an asymmetric depolarizing channel or 2-way QECCs EDP. The QPA protocol or Recurrence protocol utilizes specific Unitary or Twirling operators to increase the effect when performed repeatedly, but this may suppress the Distillation effect.

[0004]

[0005] To solve the above-described problems, the present disclosure provides a device and method capable of performing a 2-1 EDP protocol in a quantum communication system.

[0006] The present disclosure provides a device and method for performing a 2-1 EDP protocol, which can increase fidelity efficiency or increase success probability performance compared to a QPA or Recurrence protocol, by presenting a transformation operator of a Bell diagonal state in a quantum communication system.

[0007] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0008]

[0009] According to various embodiments of the present disclosure, a method for operating a first node in a communication system is provided, the method comprising: receiving at least one synchronization signal from a second node; receiving control information from the second node; determining a plurality of coefficients for a Bell diagonal state by estimating a channel between the first node and the second node; determining an operator for transforming the Bell diagonal state into a Werner state or another Bell diagonal state having different coefficients based on a target output state; performing an entanglement distillation protocol (EDP) based on the operator; and preserving one or more EPR (Einstein-Podolsky-Rosen) states related to a result of performing the EDP based on a result of performing the EDP, or discarding one or more EPR states related to a result of performing the EDP, and repeatedly performing the EDP.

[0010] According to various embodiments of the present disclosure, a method for operating a second node in a communication system is provided, the method comprising: transmitting at least one synchronization signal to a first node; transmitting control information to the first node; determining a plurality of coefficients for a Bell diagonal state by estimating a channel between the first node and the second node; determining an operator for transforming the Bell diagonal state into a Werner state or another Bell diagonal state having different coefficients based on a target output state; performing an entanglement distillation protocol (EDP) based on the operator; and preserving one or more EPR (Einstein-Podolsky-Rosen) states related to a result of performing the EDP based on a result of performing the EDP or discarding one or more EPR states related to a result of performing the EDP and repeatedly performing the EDP.

[0011] According to various embodiments of the present disclosure, in a communication system, a first node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0012] According to various embodiments of the present disclosure, in a communication system, a second node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0013] According to various embodiments of the present disclosure, a control device for controlling a first node in a communication system is provided, the control device including at least one processor, and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, the operations including all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0014] According to various embodiments of the present disclosure, a control device for controlling a second node in a communication system is provided, the control device including at least one processor, and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, the operations including all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0015] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions, based on being executed by one or more processors, perform operations, the operations comprising all steps of a method of operating a first node according to various embodiments of the present disclosure, are provided.

[0016] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions, when executed by one or more processors, perform operations, the operations comprising all steps of a method of operating a second node according to various embodiments of the present disclosure, are provided.

[0017]

[0018] To solve the above-described problems, the present disclosure can provide a device and method capable of performing a 2-1 EDP protocol in a quantum communication system.

[0019] The present disclosure provides a method for performing a 2-1 EDP protocol that can increase fidelity efficiency or success probability performance compared to a QPA or Recurrence protocol by presenting a transformation operator of a Bell diagonal state in a quantum communication system.

[0020]

[0021] The accompanying drawings are intended to aid in understanding the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0022] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0023] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0024] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0025] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0026] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0027] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0028] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0029] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0030] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0031] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0032] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0033] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0034] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0035] Figure 14 is a diagram illustrating an example of a THz communication application.

[0036] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0037] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0038] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0039] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0040] Figure 19 is a drawing showing the structure of an optical modulator.

[0041] FIG. 20 is a diagram illustrating an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.

[0042] FIG. 21 is a diagram illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0043] FIG. 22 is a diagram illustrating an example of a quantum channel model based on environmental decoherence in a system applicable to the present disclosure.

[0044] FIG. 23 is a diagram illustrating an example of an encoding circuit and a safety operator syndrome measurement circuit in a system applicable to the present disclosure.

[0045] FIG. 24 is a diagram illustrating an example of the success probability of the QPA protocol in a system applicable to the present disclosure.

[0046] FIG. 25 is a diagram illustrating an example of a process of performing a QPA protocol in a system applicable to the present disclosure.

[0047] FIG. 26 is a diagram illustrating an example of the fidelity performance of the QPA protocol in a system applicable to the present disclosure.

[0048] FIG. 27 is a diagram illustrating an example of a QECCs-based hashing protocol in a system applicable to the present disclosure.

[0049] FIG. 28 is a diagram illustrating an example of the structure of a single round of a bidirectional EDP technique in a system applicable to the present disclosure.

[0050] FIG. 29 is a diagram illustrating an example of a process in which a transmitter and a receiver measure qubits in a system applicable to the present disclosure.

[0051] FIG. 30 is a diagram illustrating an example of the structure of a unidirectional EDP technique in a system applicable to the present disclosure.

[0052] FIG. 31 is a diagram illustrating an example of the structure of an adaptive mode EDP technique in a system applicable to the present disclosure.

[0053] FIG. 32 is a drawing illustrating an example of a block diagram of a bidirectional EDP technique based on quantum error correcting codes (QECCs) in a system applicable to the present disclosure.

[0054] FIG. 33 is a diagram illustrating an example of the fidelity performance of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0055] FIG. 34 is a diagram illustrating an example of the success probability performance of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0056] FIG. 35 is a diagram illustrating an example of the fidelity performance of an adaptive mode EDP based on QECCs in a system applicable to the present disclosure.

[0057] FIG. 36 is a diagram illustrating an example of the success probability performance of an adaptive mode EDP based on QECCs in a system applicable to the present disclosure.

[0058] FIG. 37 is a diagram illustrating an example of fidelity estimation of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0059] FIG. 38 is a diagram illustrating an example of fidelity estimation of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0060] FIG. 39 is a diagram illustrating an example of estimation of the success probability of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0061] FIG. 40 is a diagram illustrating an example of estimation of the success probability of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0062] FIG. 41 is a drawing showing an example of an overall block diagram of an adaptive mode EDP technique in a system applicable to the present disclosure.

[0063] FIG. 42 is a diagram illustrating an example of the average and minimum fidelity performance of a two-way EDP in a system applicable to the present disclosure.

[0064] FIG. 43 is a diagram illustrating an example of the entire process of an Adaptive mode-based EDP technique considering minimum fidelity in a system applicable to the present disclosure.

[0065] FIG. 44 is a diagram illustrating an example of minimum fidelity estimation performance when using a two-way [[7,1,3]] error correction code in a system applicable to the present disclosure.

[0066] FIG. 45 is a diagram illustrating an example of minimum fidelity estimation performance when a two-way [[5,1,3]] error correction code is used in a system applicable to the present disclosure.

[0067] FIG. 46 is a diagram illustrating an example of the entire process of a minimum fidelity-based parameter estimation and an adaptive mode-based EDP technique in a system applicable to the present disclosure.

[0068] FIG. 47 is a diagram illustrating an example of the entire process of a Two-way QECCs EDP protocol for reusing entangled qubits in a system applicable to the present disclosure.

[0069] Figure 48 shows the probability of success (P) according to the value of α when using the [[5,1,3]] quantum error correction code in a system applicable to the present disclosure. succ ) is a drawing showing an example.

[0070] FIG. 49 is a diagram illustrating an example of the average fidelity (F') of an EPR pair according to the value of α when utilizing a [[5,1,3]] quantum error correction code in a system applicable to the present disclosure.

[0071] FIG. 50 is a diagram illustrating an example of a process for performing additional resource reduction in a Two-way QECCs EDP in a system applicable to the present disclosure.

[0072] FIG. 51 is a diagram illustrating an example of quantum teleportation (QT) in a system applicable to the present disclosure.

[0073] FIG. 52 is a diagram illustrating an example of a two-step QSDC protocol in a system applicable to the present disclosure.

[0074] FIG. 53 is a diagram illustrating an example of an EDP performing circuit after a transformation process of a Bell diagonal state utilizing various unitary operations in a system applicable to the present disclosure.

[0075] FIG. 54 is a diagram illustrating an example of a recurrence protocol (an EDP performing circuit after changing the Werner state by twirling) in a system applicable to the present disclosure.

[0076] FIG. 55 is a diagram illustrating an example of a process for performing a 2-1 EDP protocol utilizing operator adaptation in a system applicable to the present disclosure.

[0077] FIG. 56 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0078] FIG. 57 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0079] FIG. 58 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0080] FIG. 59 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0081] FIG. 60 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0082] Figure 61 illustrates a signal processing circuit for a transmission signal.

[0083] FIG. 62 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.

[0084] FIG. 63 illustrates a mobile device applicable to various embodiments of the present disclosure.

[0085] FIG. 64 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0086] FIG. 65 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0087] FIG. 66 illustrates an XR device applicable to various embodiments of the present disclosure.

[0088] FIG. 67 illustrates a robot applicable to various embodiments of the present disclosure.

[0089] FIG. 68 illustrates an AI device applicable to various embodiments of the present disclosure.

[0090]

[0091] In various embodiments of the present disclosure, “A or B” may mean “only A,” “only B,” or “both A and B.” In other words, in various embodiments of the present disclosure, “A or B” may be interpreted as “A and / or B.” For example, in various embodiments of the present disclosure, “A, B or C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.”

[0092] In various embodiments of the present disclosure, a slash ( / ) or a comma may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B, or C."

[0093] In various embodiments of the present disclosure, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Furthermore, in various embodiments of the present disclosure, the expressions “at least one of A or B” or “at least one of A and / or B” may be interpreted as equivalent to “at least one of A and B.”

[0094] Additionally, in various embodiments of the present disclosure, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0095] Additionally, parentheses used in various embodiments of the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in various embodiments of the present disclosure is not limited to "PDCCH", and "PDDCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0096] Technical features individually described in a single drawing in various embodiments of the present disclosure may be implemented individually or simultaneously.

[0097]

[0098] The following technologies can be used in various wireless access systems, such as CDMA, FDMA, TDMA, OFDMA, and SC-FDMA. CDMA can be implemented using wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (Global System for Mobile communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is a part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is a part of E-UMTS (Evolved UMTS) that uses E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A / LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

[0099]

[0100] For clarity, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE refers to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. “xxx” refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system. For background technology, terms, abbreviations, etc. used in the description of the present disclosure, reference may be made to matters described in standard documents published prior to the present disclosure. For example, reference may be made to the following documents.

[0101]

[0102] 3GPP LTE

[0103] - 36.211: Physical channels and modulation

[0104] - 36.212: Multiplexing and channel coding

[0105] - 36.213: Physical layer procedures

[0106] - 36.300: Overall description

[0107] - 36.331: Radio Resource Control (RRC)

[0108] 3GPP NR

[0109] - 38.211: Physical channels and modulation

[0110] - 38.212: Multiplexing and channel coding

[0111] - 38.213: Physical layer procedures for control

[0112] - 38.214: Physical layer procedures for data

[0113] - 38.300: NR and NG-RAN Overall Description

[0114] - 38.331: Radio Resource Control (RRC) protocol specification

[0115]

[0116] Physical Channel and Frame Structure

[0117] Physical channels and general signal transmission

[0118] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0119] In a wireless communication system, a terminal receives information from a base station via the downlink (DL) and transmits it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0120]

[0121] When a terminal is powered on or enters a new cell, it performs an initial cell search operation, such as synchronizing with the base station (S11). To this end, the terminal receives a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS) from the base station to synchronize with the base station and obtain information such as a cell ID. Afterwards, the terminal can receive a Physical Broadcast Channel (PBCH) from the base station to obtain broadcast information within the cell. Meanwhile, the terminal can receive a Downlink Reference Signal (DL RS) during the initial cell search phase to check the downlink channel status.

[0122]

[0123] A terminal that has completed initial cell search can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) based on information contained in the PDCCH (S12).

[0124]

[0125] Meanwhile, when accessing a base station for the first time or when there are no radio resources for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (S13 to S16). To this end, the terminal may transmit a specific sequence as a preamble via a physical random access channel (PRACH) (S13 and S15) and receive a response message (RAR (Random Access Response) message) to the preamble via a PDCCH and a corresponding PDSCH. In the case of a contention-based RACH, a contention resolution procedure may additionally be performed (S16).

[0126]

[0127] The terminal that has performed the procedure described above can then perform PDCCH / PDSCH reception (S17) and physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) transmission (S18) as general uplink / downlink signal transmission procedures. In particular, the terminal can receive downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats can be applied depending on the purpose of use.

[0128]

[0129] Meanwhile, the control information that the terminal transmits to the base station via the uplink or that the terminal receives from the base station may include downlink / uplink ACK / NACK signals, CQI (Channel Quality Indicator), PMI (Precoding Matrix Index), RI (Rank Indicator), etc. The terminal may transmit the above-described control information such as CQI / PMI / RI via PUSCH and / or PUCCH.

[0130]

[0131] Structure of uplink and downlink channels

[0132] Downlink channel structure

[0133] The base station transmits a related signal to the terminal through a downlink channel described below, and the terminal receives the related signal from the base station through a downlink channel described below.

[0134]

[0135] (1) Physical Downlink Shared Channel (PDSCH)

[0136] PDSCH carries downlink data (e.g., DL-shared channel transport block, DL-SCH TB) and applies modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM. Codewords are generated by encoding the TBs. PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword are mapped to one or more layers (Layer mapping). Each layer is mapped to resources along with a Demodulation Reference Signal (DMRS), generated as an OFDM symbol signal, and transmitted through the corresponding antenna port.

[0137]

[0138] (2) Physical downlink control channel (PDCCH)

[0139] The PDCCH carries downlink control information (DCI) and employs modulation methods such as QPSK. A PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs), depending on the Aggregation Level (AL). Each CCE is comprised of six Resource Element Groups (REGs). A REG is defined by one OFDM symbol and one (P)RB.

[0140] The UE acquires DCI transmitted via the PDCCH by performing decoding (also known as blind decoding) on ​​a set of PDCCH candidates. The set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE can acquire DCI by monitoring PDCCH candidates within one or more search space sets established by the MIB or higher layer signaling.

[0141]

[0142] Uplink channel structure

[0143] The terminal transmits a related signal to the base station through the uplink channel described below, and the base station receives the related signal from the terminal through the uplink channel described below.

[0144] (1) Physical Uplink Shared Channel (PUSCH)

[0145] PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and / or uplink control information (UCI), and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform, a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform, etc. When the PUSCH is transmitted based on a DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is disabled (e.g., transform precoding is disabled), the UE transmits the PUSCH based on the CP-OFDM waveform, and when transform precoding is enabled (e.g., transform precoding is enabled), the UE can transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants in DCI, or semi-statically scheduled (configured grant) based on higher layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)). PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0146] (2) Physical Uplink Control Channel (PUCCH)

[0147] PUCCH carries uplink control information, HARQ-ACK and / or scheduling request (SR), and can be divided into multiple PUCCHs depending on the PUCCH transmission length.

[0148]

[0149] Below, we describe new radio access technology (new RAT, NR).

[0150] As more and more communication devices demand greater communication capacity, the need for improved mobile broadband communication compared to existing radio access technology (RAT) is emerging. Furthermore, massive Machine Type Communications (MTC), which connects numerous devices and objects to provide various services anytime, anywhere, is also a key issue to be considered in next-generation communication. Furthermore, communication system design that considers reliability and latency-sensitive services / terminals is being discussed. The introduction of next-generation radio access technologies that take into account enhanced mobile broadband communication, massive MTC, and URLLC (Ultra-Reliable and Low Latency Communication) is being discussed, and in various embodiments of the present disclosure, these technologies are conveniently referred to as new RAT or NR.

[0151]

[0152] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0153] Referring to FIG. 2, the NG-RAN may include a gNB and / or an eNB that provides user plane and control plane protocol termination to the UE. FIG. 1 illustrates a case where only a gNB is included. The gNB and eNB are connected to each other via an Xn interface. The gNB and eNB are connected to the 5th generation core network (5G Core Network: 5GC) via the NG interface. More specifically, the gNB is connected to the access and mobility management function (AMF) via the NG-C interface, and the gNB is connected to the user plane function (UPF) via the NG-U interface.

[0154]

[0155] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0156] Referring to FIG. 3, the gNB can provide functions such as inter-cell radio resource management (Inter Cell RRM), radio bearer management (RB control), connection mobility control (Connection Mobility Control), radio admission control (Radio Admission Control), measurement configuration and provision, and dynamic resource allocation. The AMF can provide functions such as NAS security and idle state mobility processing. The UPF can provide functions such as mobility anchoring and PDU processing. The SMF (Session Management Function) can provide functions such as terminal IP address allocation and PDU session control.

[0157]

[0158] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0159] The 5G usage scenario illustrated in FIG. 4 is merely exemplary, and the technical features of various embodiments of the present disclosure can also be applied to other 5G usage scenarios not illustrated in FIG. 4.

[0160] Referring to Figure 4, the three key requirements areas for 5G include (1) enhanced mobile broadband (eMBB), (2) massive machine type communication (mMTC), and (3) ultra-reliable and low latency communications (URLLC). Some use cases may require optimization across multiple areas, while others may focus on just one key performance indicator (KPI). 5G supports these diverse use cases in a flexible and reliable manner.

[0161] eMBB focuses on improving data speeds, latency, user density, and overall capacity and coverage of mobile broadband connections. It targets throughputs of around 10 Gbps. eMBB significantly exceeds basic mobile internet access, enabling rich interactive experiences, media and entertainment applications in the cloud, and augmented reality. Data is a key driver of 5G, and for the first time, dedicated voice services may not be available in the 5G era. In 5G, voice is expected to be handled as an application, simply using the data connection provided by the communication system. The increased traffic volume is primarily due to the increasing content size and the growing number of applications that require high data rates. Streaming services (audio and video), interactive video, and mobile internet connectivity will become more prevalent as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are rapidly growing on mobile communication platforms, and this can be applied to both work and entertainment. Cloud storage is a particular use case driving the growth of uplink data rates. 5G is also used for remote work in the cloud, requiring significantly lower end-to-end latency to maintain a superior user experience when tactile interfaces are used. In entertainment, for example, cloud gaming and video streaming are other key factors driving the demand for mobile broadband. Entertainment is essential on smartphones and tablets, regardless of location, including in highly mobile environments like trains, cars, and airplanes. Another use case is augmented reality and information retrieval for entertainment, where augmented reality requires extremely low latency and instantaneous data volumes.

[0162] mMTC is designed to enable communication between a large number of low-cost, battery-powered devices, supporting applications such as smart metering, logistics, field, and body sensors. mMTC targets a battery life of approximately 10 years and / or a population of approximately 1 million devices per square kilometer. mMTC enables seamless connectivity of embedded sensors across all sectors and is one of the most anticipated 5G use cases. The number of IoT devices is projected to reach 20.4 billion by 2020. Industrial IoT is one area where 5G will play a key role, enabling smart cities, asset tracking, smart utilities, agriculture, and security infrastructure.

[0163] URLLC is ideal for vehicle communications, industrial control, factory automation, remote surgery, smart grids, and public safety applications by enabling devices and machines to communicate with high reliability, very low latency, and high availability. URLLC targets latency on the order of 1 ms. URLLC encompasses new services that will transform industries through ultra-reliable, low-latency links, such as remote control of critical infrastructure and autonomous vehicles. This level of reliability and latency is essential for smart grid control, industrial automation, robotics, and drone control and coordination.

[0164] Next, we will look more specifically at a number of usage examples included within the triangle in Fig. 4.

[0165] 5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) by delivering streams rated at hundreds of megabits per second to gigabits per second. These high speeds may be required to deliver TV at resolutions beyond 4K (6K, 8K, and beyond), as well as virtual reality (VR) and augmented reality (AR). VR and AR applications include near-immersive sports events. Certain applications may require specialized network configurations. For example, for VR gaming, a gaming company may need to integrate its core servers with the network operator's edge network servers to minimize latency.

[0166] Automotive is expected to be a significant new driver for 5G, with numerous use cases for in-vehicle mobile communications. For example, passenger entertainment demands both high capacity and high mobile broadband, as future users will consistently expect high-quality connectivity regardless of their location and speed. Another automotive application is augmented reality dashboards. An AR dashboard allows drivers to identify objects in the dark on top of what they see through the windshield. The AR dashboard overlays information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable vehicle-to-vehicle communication, information exchange between vehicles and supporting infrastructure, and information exchange between vehicles and other connected devices (e.g., devices accompanying pedestrians). Safety systems can guide drivers to safer driving behaviors, reducing the risk of accidents. The next step will be remotely controlled or autonomous vehicles, which require highly reliable and fast communication between different autonomous vehicles and / or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving tasks, leaving drivers to focus solely on traffic anomalies that the vehicle itself cannot detect. The technological requirements for autonomous vehicles will require ultra-low latency and ultra-high-speed reliability, increasing traffic safety to levels unattainable by humans.

[0167] Smart cities and smart homes, often referred to as smart societies, will be embedded with dense wireless sensor networks. A distributed network of intelligent sensors will identify conditions for cost- and energy-efficient maintenance of cities or homes. Similar setups can be implemented for individual homes. Temperature sensors, window and heating controllers, burglar alarms, and appliances will all be wirelessly connected. Many of these sensors typically require low data rates, low power, and low cost. However, for example, real-time HD video may be required from certain types of devices for surveillance purposes.

[0168] The consumption and distribution of energy, including heat and gas, are becoming increasingly decentralized, requiring automated control of distributed sensor networks. Smart grids interconnect these sensors using digital information and communication technologies to collect and act on information. This information can include the behavior of suppliers and consumers, enabling smart grids to improve efficiency, reliability, economic efficiency, sustainable production, and the automated distribution of fuels like electricity. Smart grids can also be viewed as another low-latency sensor network.

[0169] The health sector has numerous applications that can benefit from mobile communications. Telecommunications systems can support telemedicine, which provides clinical care in remote locations. This can help reduce distance barriers and improve access to health services that are otherwise unavailable in remote rural areas. It can also be used to save lives in critical care and emergency situations. Mobile-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0170] Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Therefore, the potential to replace cables with reconfigurable wireless links presents an attractive opportunity for many industries. However, achieving this requires wireless connections to operate with similar latency, reliability, and capacity to cables, while simplifying their management. Low latency and extremely low error rates are new requirements for 5G connectivity.

[0171] Logistics and freight tracking are important use cases for mobile communications, enabling the tracking of inventory and packages anywhere using location-based information systems. Logistics and freight tracking typically require low data rates but may require wide-range and reliable location information.

[0172] Hereinafter, examples of next-generation communications (e.g., 6G) that can be applied to various embodiments of the present disclosure will be described.

[0173]

[0174] 6G system in general

[0175] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements as shown in Table 1 below. In other words, Table 1 is a table showing an example of the requirements of a 6G system.

[0176]

[0177] Per device peak data rate1TbpsE2E latency1msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0178]

[0179] 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0180]

[0181] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0182] 6G systems are expected to have 50 times the simultaneous wireless connectivity of 5G systems. URLLC, a key feature of 5G, will become even more crucial in 6G communications by providing end-to-end latency of less than 1 ms. 6G systems will have significantly higher volumetric spectral efficiency, compared to the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems. New network characteristics in 6G may include:

[0183] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system is crucial for 6G.

[0184] Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, upgrading the wireless evolution from "connected objects" to "connected intelligence." AI can be applied at every stage of the communication process (or at every signal processing step, as described below).

[0185] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0186] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0187] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0188] - Small cell networks: The concept of small cell networks was introduced to improve received signal quality in cellular systems by increasing throughput, energy efficiency, and spectral efficiency. Consequently, small cell networks are essential for 5G and beyond-5G (5GB) communication systems. Accordingly, 6G communication systems also adopt the characteristics of small cell networks.

[0189] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0190] High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems may be potential solutions to this problem.

[0191] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0192] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0193]

[0194] Core implementation technology of 6G systems

[0195]

[0196] Artificial Intelligence

[0197] The most crucial and newly introduced technology for 6G systems is AI. 4G systems did not involve AI. 5G systems will support partial or very limited AI. However, 6G systems will fully support AI for automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analyses to determine how complex target tasks should be performed. In other words, AI can increase efficiency and reduce processing delays.

[0198] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0199] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0200] Machine learning can be used for channel estimation and channel tracking, as well as for power allocation and interference cancellation in the physical layer of the downlink (DL). Furthermore, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0201] However, the application of DNN for transmission at the physical layer may have the following problems.

[0202] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0203] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks that detect complex-domain signals to match the characteristics of wireless communication signals.

[0204] Below, we will look at machine learning in more detail.

[0205] Machine learning refers to a series of operations that train machines to perform tasks that humans can or cannot perform. Machine learning requires data and a learning model. Data learning methods in machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0206] Neural network training aims to minimize output errors. It involves repeatedly inputting training data into a neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error.

[0207] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. Labeled training data is input to a neural network, and the error can be calculated by comparing the output (categories) of the neural network with the training data labels. The calculated error is backpropagated through the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated through backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the network to reach a certain level of performance, thereby improving efficiency. In the later stages of training, a low learning rate can be used to improve accuracy.

[0208] Learning methods may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted by a transmitter in a communication system, supervised learning is preferable to unsupervised learning or reinforcement learning.

[0209] The learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0210] The neural network cores used in learning methods are mainly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machines (RNN).

[0211] An artificial neural network is an example of a network of multiple perceptrons.

[0212]

[0213] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0214] Referring to Fig. 6, when an input vector x=(x1,x2,...,xd) is input, the entire process of multiplying each component by a weight (W1,W2,...,Wd), adding up all the results, and then applying the activation function σ(·) is called a perceptron. A large-scale artificial neural network structure can extend the simplified perceptron structure illustrated in Fig. 6 to apply the input vector to perceptrons of different dimensions. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0215] Meanwhile, the perceptron structure illustrated in Fig. 6 can be explained as consisting of a total of three layers based on input and output values. An artificial neural network in which there are H perceptrons of (d+1) dimensions between the 1st layer and the 2nd layer, and K perceptrons of (H+1) dimensions between the 2nd layer and the 3rd layer can be expressed as in Fig. 7.

[0216]

[0217] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0218] The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. The example in Fig. 7 shows three layers, but when counting the number of layers in an actual artificial neural network, the input layer is excluded, so it can be viewed as a total of two layers. An artificial neural network is composed of perceptrons, which are basic blocks, connected in two dimensions.

[0219] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0220]

[0221] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0222] The deep neural network illustrated in Figure 8 is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is referred to as a fully connected neural network. In a fully connected neural network, there is no connection between nodes located in the same layer, and there is a connection only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0223] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0224]

[0225] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0226] In DNN, nodes within a single layer are arranged vertically in a one-dimensional manner. However, Fig. 9 can assume a case where nodes are arranged two-dimensionally, with w nodes in width and h nodes in height (the convolutional neural network structure of Fig. 9). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of hΥw weights must be considered. Since there are hΥw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0227] The convolutional neural network of Fig. 9 has a problem in that the number of weights increases exponentially according to the number of connections. Therefore, instead of considering the connections of all modes between adjacent layers, it assumes that there are small filters, and performs weighted sum and activation function operations on the overlapping portions of the filters, as in Fig. 10.

[0228]

[0229] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0230] Each filter has a weight corresponding to the number of its size, and weight learning can be performed so that a specific feature on the image can be extracted as a factor and output. In Fig. 10, a filter of size 3Y3 is applied to the upper left 3Y3 region of the input layer, and the output value resulting from performing weighted sum and activation function operations on the corresponding node is stored in z22.

[0231] The above filter performs weighted sum and activation function operations while moving at a certain horizontal and vertical interval while scanning the input layer, and places the output value at the current filter position. This operation method is similar to the convolution operation for images in the field of computer vision, so a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. In addition, a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0232] In the convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located in the area covered by the filter, starting from the node where the current filter is located. This allows a single filter to focus on features within a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important criterion for judgment. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can generate multiple output results through the convolution operation of each filter.

[0233] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence is called a recurrent neural network structure.

[0234]

[0235] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0236] Referring to Figure 11, a recurrent neural network (RNN) is a structure that inputs elements (x1(t), x2(t), ,..., xd(t)) of a data sequence at a time point t into a fully connected neural network, and then inputs the hidden vectors (z1(t-1), z2(t-1),..., zH(t-1)) of the immediately preceding time point t-1 together and applies a weighted sum and activation function. The reason for transmitting the hidden vector to the next time point in this way is because the information in the input vectors of the preceding time points is considered to be accumulated in the hidden vector of the current time point.

[0237]

[0238] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0239] Referring to Figure 12, the recurrent neural network operates in a predetermined order of time for the input data sequence.

[0240] When the input vector (x1(t), x2(t), ,..., xd(t)) at time point 1 is input to the recurrent neural network, the hidden vector (z1(1), z2(1),..., zH(1)) is input together with the input vector (x1(2), x2(2),..., xd(2)) at time point 2, and the vector (z1(2), z2(2),..., zH(2)) of the hidden layer is determined through a weighted sum and an activation function. This process is repeatedly performed until time points 2, 3, ,,, T.

[0241] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0242] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0243] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0244] THz (Terahertz) communication

[0245] Data rates can be increased by increasing bandwidth. This can be achieved by utilizing sub-THz communications with wide bandwidths and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz, with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (sub-THz band) is considered a key part of the THz band for cellular communications. Adding the sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0246]

[0247] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0248] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0249] Optical wireless technology

[0250] OWC technology is designed for 6G communications, in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul networks. OWC technology has already been used in 4G communication systems, but it will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and wideband-based FSO communication are already well-known. Communications based on optical wireless technology can provide very high data rates, low latency, and secure communications. LiDAR can also be used for ultra-high-resolution 4D mapping in 6G communications based on wideband.

[0251] FSO backhaul network

[0252] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber-optic network. Therefore, data transmission in an FSO system is similar to that of a fiber-optic system. Therefore, FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks. Using FSO, ultra-long-distance communications are possible, even over distances exceeding 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas, such as the ocean, space, underwater, and isolated islands. FSO also supports cellular base station (BS) connections.

[0253] Massive MIMO technology

[0254] One of the key technologies for improving spectral efficiency is the application of MIMO technology. As MIMO technology improves, spectral efficiency also improves. Therefore, massive MIMO technology will be crucial in 6G systems. Because MIMO technology utilizes multiple paths, multiplexing technology must be considered to ensure that data signals can be transmitted along more than one path, as well as beam generation and operation technologies suitable for the THz band.

[0255] Blockchain

[0256] Blockchain will become a crucial technology for managing massive amounts of data in future communication systems. Blockchain is a form of distributed ledger technology. A distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchains are managed by a peer-to-peer network and can exist without being managed by a central authority or server. Data on a blockchain is collected and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain perfectly complements large-scale IoT with its inherently enhanced interoperability, security, privacy, reliability, and scalability. Therefore, blockchain technology offers several features, such as interoperability between devices, traceability of large amounts of data, autonomous interaction with other IoT systems, and the massive connectivity stability of 6G communication systems.

[0257] 3D networking

[0258] 6G systems integrate terrestrial and airborne networks to support vertically expanded user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in altitude and associated degrees of freedom, 3D connections differ significantly from existing 2D networks.

[0259] Quantum communication

[0260] Unsupervised reinforcement learning holds promise in the context of 6G networks. Supervised learning approaches cannot label the massive amounts of data generated by 6G networks. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows for truly autonomous network operation.

[0261] drone

[0262] Unmanned Aerial Vehicles (UAVs), or drones, will be a key element in 6G wireless communications. In most cases, high-speed wireless connections will be provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs offer specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communication infrastructure is not economically feasible, and sometimes, volatile environments make it impossible to provide services. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0263] Cell-free Communication

[0264] Tight integration of multiple frequencies and heterogeneous communication technologies is crucial in 6G systems. As a result, users will be able to seamlessly move from one network to another without requiring any manual configuration on their devices. The best network will be automatically selected from available communication technologies. This will break the limitations of the cell concept in wireless communications. Currently, user movement from one cell to another in dense networks results in excessive handovers, resulting in handover failures, handover delays, data loss, and a ping-pong effect. 6G cell-free communications will overcome all of these challenges and provide better QoS. Cell-free communications will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as heterogeneous radios on devices.

[0265] Integration of wireless information and energy transmission

[0266] WIET uses the same fields and waves as wireless communication systems. Specifically, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-powered wireless systems. Therefore, battery-less devices will be supported by 6G communications.

[0267] Integration of sensing and communication

[0268] Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

[0269] Integration of Access Backhaul Networks

[0270] In 6G, the density of access networks will be enormous. Each access network will be connected to backhaul connections, such as fiber optics and FSO networks. To accommodate the massive number of access networks, there will be tight integration between access and backhaul networks.

[0271] Holographic beam forming

[0272] Beamforming is a signal processing procedure that adjusts an antenna array to transmit a wireless signal in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, including high signal-to-noise ratio, interference avoidance and rejection, and high network efficiency. Holographic beamforming (HBF) is a novel beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a highly effective approach for efficient and flexible signal transmission and reception in multi-antenna communication devices in 6G.

[0273] Big data analysis

[0274] Big data analytics is a complex process for analyzing diverse, large-scale data sets, or "big data." This process uncovers hidden data, unknown correlations, and customer trends, ensuring complete data management. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process massive amounts of data in 6G systems.

[0275] Large Intelligent Surface (LIS)

[0276] THz-band signals have strong linearity, which can create many shadow areas due to obstacles. LIS technology, which enables expanded communication coverage, enhanced communication stability, and additional value-added services by installing LIS near these shadow areas, is becoming increasingly important. LIS is an artificial surface made of electromagnetic materials that can alter the propagation of incoming and outgoing radio waves. While LIS can be viewed as an extension of massive MIMO, it differs from massive MIMO in its array structure and operating mechanism. Furthermore, LIS operates as a reconfigurable reflector with passive elements, passively reflecting signals without using active RF chains, which offers the advantage of low power consumption. Furthermore, because each passive reflector in LIS must independently adjust the phase shift of the incoming signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift via the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0277]

[0278] Terahertz (THz) wireless communications in general

[0279]

[0280] THz wireless communication uses THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and can refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared light, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high linearity and can focus beams. In addition, since the photon energy of THz waves is only a few meV, they have the characteristic of being harmless to the human body. The frequency bands expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz), which have low propagation loss due to molecular absorption in the air. Discussions on standardization of THz wireless communication are being centered around the IEEE 802.15 THz working group in addition to 3GPP, and standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) may specify or supplement the contents described in various embodiments of the present disclosure. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0281]

[0282] Figure 14 is a diagram illustrating an example of a THz communication application.

[0283] As illustrated in Figure 14, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as indoor small cells, wireless connections in data centers, and near-field communications, such as kiosk downloads.

[0284] Table 2 below shows examples of technologies that can be used in THz waves.

[0285] Transceivers DeviceAvailable immature: UTC-PD, RTD and SBDModulation and CodingLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboAntennaOmni and Directional, phased array with low number of antenna elementsBandwidth69GHz (or 23 GHz) at 300GHzChannel modelsPartiallyData rate100GbpsOutdoor deploymentNoFree space lossHighCoverageLowRadio Measurements300GHz indoorDevice sizeFew micrometers

[0286]

[0287] THz wireless communications can be categorized based on the methods used to generate and receive THz waves. THz generation methods can be categorized as either optical or electronic-based.

[0288]

[0289] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0290] Methods for generating THz using electronic components include a method using semiconductor components such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a MMIC (Monolithic Microwave Integrated Circuits) method using an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor), and a method using a Si-CMOS-based integrated circuit. In the case of Fig. 15, a multiplier (doubler, tripler, multiplier) is applied to increase the frequency, and it passes through a subharmonic mixer and is radiated by an antenna. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, and matches it to the desired harmonic frequency and filters out all remaining frequencies. In addition, beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 15. In Fig. 15, IF represents intermediate frequency, tripler and multiplexer represent multipliers, PA represents power amplifier, LNA represents low noise amplifier, and PLL represents phase-locked loop.

[0291]

[0292] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0293] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0294] Optical component-based THz wireless communication technology refers to a method of generating and modulating THz signals using optical components. Optical component-based THz signal generation technology generates an ultra-high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. Compared to technologies that use only electronic components, this technology can easily increase the frequency, generate high-power signals, and obtain flat response characteristics over a wide frequency band. As illustrated in Figure 16, optical component-based THz signal generation requires a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector. In the case of Figure 16, the light signals of two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 16, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a UTC-PD (Uni-Travelling Carrier Photo-Detector) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons with bandgap grading. The UTC-PD is capable of detecting light at 150 GHz or higher. In Fig. 17, an EDFA (Erbium-Doped Fiber Amplifier) ​​represents an erbium-doped fiber amplifier, a PD (Photo Detector) represents a semiconductor device that can convert an optical signal into an electrical signal, an OSA represents an optical module (Optical Sub Assembly) that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component, and a DSO represents a digital storage oscilloscope.

[0295]

[0296] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0297] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0298] Figure 19 is a drawing showing the structure of an optical modulator.

[0299] In general, the phase of a signal can be changed by passing the optical source of a laser through an optical wave guide. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An opto-electrical modulator (O / E converter) can generate THz pulses by optical rectification operation by a nonlinear crystal, photoelectric conversion by a photoconductive antenna, emission from a bunch of relativistic electrons, etc. Terahertz pulses generated in the above manner can have a length in units of femtoseconds to picoseconds. An optical / electronic converter (O / E converter) performs down conversion by utilizing the non-linearity of the device.

[0300] Considering the THz spectrum usage, it is likely that THz systems will use multiple contiguous gigahertz bands for fixed or mobile service purposes. Based on the outdoor scenario criteria, the available bandwidth can be classified based on the oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the THz pulse length for one carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0301] Effective down-conversion from the infrared band (IR band) to the terahertz band (THz band) depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired terahertz band (THz band), it is necessary to design an optical / electrical converter (O / E converter) with the most ideal non-linearity for transferring to the corresponding terahertz band (THz band). If an optical / electrical converter (O / E converter) that is not suitable for the target frequency band is used, errors are likely to occur in the amplitude and phase of the corresponding pulse.

[0302] In a single-carrier system, a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter. Depending on the channel environment, in a multi-carrier system, the number of optical-to-electrical converters may be equal to the number of carriers. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-converted using an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency region of the specific resource region may include multiple chunks. Each chunk may be composed of at least one component carrier (CC).

[0303]

[0304] Specific descriptions of various embodiments of the present disclosure

[0305] Hereinafter, various embodiments of the present disclosure will be described in more detail.

[0306]

[0307] The present disclosure relates to a method for configuring entanglement distillation of EPR (Einstein-Podolsky-Rosen) pairs using quantum error correction codes and error detection codes, taking into account minimum fidelity in a quantum communication system. Considering that fidelity may vary depending on measurement syndromes in quantum entanglement distillation techniques based on quantum error correction codes, the present disclosure applies a method for sharing minimum fidelity between transmitters and receivers and a parameter estimation technique based on minimum fidelity. As a result, the transmitter and receiver accurately share the fidelity of the EPR pair generated after EDP, and a highly efficient entangled pair generation method is proposed that satisfies fidelity conditions regardless of the syndrome being measured.

[0308]

[0309] Background to various embodiments of the present disclosure

[0310] Bell state and Bell basis

[0311] The Bell state is the simplest example of quantum entanglement. It refers to the four quantum states formed by two qubits in a maximally entangled state, as shown in Equation 1 below. This can be viewed as a maximally entangled basis for the four-dimensional Hilbert space for the two qubits, and is called the Bell basis.

[0312]

[0313] Creation of a bell state

[0314] FIG. 20 is a diagram illustrating an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.

[0315] The Bell state can be generated through a two-qubit quantum circuit consisting of a Hadamard gate and a CNOT gate (controlled not gate), as shown in Fig. 20. Four two-qubit inputs It has a bell state output as shown in Table 3. Table 3 shows the input / output states of the bell state generation circuit.

[0316]

[0317] Bell state measurement / Bell state analysis

[0318] FIG. 21 is a diagram illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0319] As previously explained, since the Bell states form an orthonormal basis, an appropriate measurement can be defined to identify the four Bell states, which is called Bell state measurement or Bell state analysis. In Bell state measurement, the goal is to determine which of the four quantum entanglement states defined by the Bell states the states of two qubits belong to. If the order of the CNOT gate and the Hadamard gate in the Bell state generation circuit of Fig. 20 is reversed, a Bell state measurement circuit as shown in Fig. 21 is obtained. Measurement results as shown in Table 4 can be obtained for the four quantum entanglement states corresponding to the Bell states. Table 4 shows the input and output states of the Bell state measurement circuit.

[0320]

[0321]

[0322] Entanglement

[0323] Entanglement is a very important property that differentiates quantum systems from classical information. Entanglement refers to a state in which the results of different observations are closely related to each other. The entanglement state of a quantum system is stronger than any entanglement state (correlation) that exists in classical mechanics. Two qubits can be represented as a superposition of four fundamental quantum states in Hilbert space. Here, the four fundamental quantum states are { } is included. The basic quantum state of two qubits can be expressed through tensor operations on the basic states of individual qubits. If the state of two qubits cannot be expressed as a tensor product of a single qubit, such a qubit state is called an entangled state. There are four cases, which are representative examples of qubits in an entangled state and are called EPR (Einstein-Podolsky-Rosen) states, and the four cases are as shown in the following mathematical expression 2.

[0324]

[0325] The above EPR state is also called the Bell state, and the measurement result of the preceding qubit always affects the measurement of the succeeding qubit. Furthermore, all four of the above pure states are maximally entangled states and constitute the vertical basis of the two-qubit Hilbert Space.

[0326] For entangled qubits with M>2 qubits, the GHZ state is as follows:

[0327]

[0328] When M=2 The Bell State is generally expressed for M = 3. Sometimes, the GHZ State can be expressed by extending it to a system corresponding to d-dimension rather than 2-dimension.

[0329]

[0330] Continuous Quantum Error

[0331] In existing information systems, information is composed of '0' and '1', and errors are represented as '0' changing to '1' or '1' changing to '0'. Qubit can be thought of as a single point on the surface of the Bloch sphere. When an error occurs in a qubit in an existing information system, it is called a bit flip error. This error means that the value of 'a' changes to the value of 'b', which means that when measuring a qubit, the measurement probability is changed from the initial value due to the error. Another type of error in an existing information system is class There is a phase flip error where the phase between them changes by 180 degrees. The error can occur with any phase along the X-axis or Z-axis, so quantum errors can have a continuous phase.

[0332]

[0333] The decay of quantum information by measurement

[0334] Quantum information exists probabilistically, and quantum information collapses into a ground state the moment it is measured and cannot be restored to the state before the measurement. The quantum information after measurement by the measurement operator has a probability |a| 2 Wow |b| 2 It collapses into one of the states of the basis that constitutes information. The collapsed information does not have the information of 'a' or 'b' and cannot return to the state before measurement.

[0335] From the perspective of quantum error-correcting codes, applying quantum error-correcting codes in quantum information systems requires generating codewords, estimating errors in the channel, and then restoring the information without measuring the information during the encoding and restoration process, or without measurements that would alter the information. In this process, continuously occurring quantum errors are digitized through measurements for error estimation.

[0336]

[0337] Quantum Error Correcting codes (QECCs)

[0338] FIG. 22 is a diagram illustrating an example of a quantum channel model based on environmental decoherence in a system applicable to the present disclosure.

[0339] Similar to classical communication, quantum communication processes can also be affected by imperfections in the real-world environment, affecting the quality of transmitted information. This interaction with the environment can lead to irreversible changes in the quantum state, a process known as decoherence. Environmental decoherence constitutes a major source of quantum state corruption, and can occur not only in quantum memory but also during quantum transmission or quantum processing. Figure 22 illustrates the relationship between quantum channel models widely used to model environmental decoherence.

[0340] Environmental decoherence can be described as an unwanted interaction between a qubit and its environment, more specifically, entanglement, which disrupts the coherent superposition of the underlying quantum state. For example, in such cases, a qubit (or quantum system) loses energy due to its interaction with the environment, such as when a qubit's excited state decays due to spontaneous emission of a photon, or when a photon is lost or absorbed during transmission through an optical fiber. This type of decoherence process can be modeled using an amplitude damping channel. Another example of environmental decoherence is known as dephasing or phase damping, which characterizes the loss of quantum information without energy loss. This can occur, for example, in cases such as scattering of a photon or perturbation of electronic states due to stray charges.

[0341] However, the amplitude damping channel or phase damping channel model results in a system with 2 N-Qubits. N It is not feasible to simulate these channels classically, because they have a Hilbert space of dimensionality. For efficient classical simulation, the amplitude and phase decay channels are Pauli channels N PIt can be approximated by mapping the input state with density operator ρ to the state as shown in mathematical expression 4 below.

[0342]

[0343] Here, I, X, Y, Z correspond to single Qubit Pauli operators and p x , p y , p z is the probability that Pauli X, Pauli Y, and Pauli Z errors will occur. Bit-flip errors corresponding to the Pauli X channel and bit-phase-flip errors corresponding to the Pauli Y channel are related to amplitude attenuation, while phase-flip errors corresponding to the Pauli Z channel are caused by phase attenuation. Most practical quantum systems are asymmetric channels, which are channels in which bit-flip, phase-flip, or bit-phase-flip errors dominate. Bit-flip, phase-flip, and bit-phase-flip errors occur with equal probability (p x =p y =p z ) A special case of the Pauli channel is called a depolarizing channel and can be mathematically expressed as in Equation 5 below.

[0344]

[0345] If one of the qubits in the state passes through the depolarization channel, the fidelity Werner state W is organized as F is defined as in mathematical formula 6 below.

[0346]

[0347]

[0348] Quantum Error Correcting codes (QECCs)

[0349] Quantum error-correcting codes utilize additional qubits in addition to information qubits to generate codewords to preserve quantum information from errors. These codewords, which experience continuous errors, are then collapsed into codewords projecting discontinuous errors through measurement, after which the errors are detected and maintained as the original codeword. Most quantum error-correcting codes utilize stabilizer codes, which have properties similar to linear error-correcting codes used in digital systems.

[0350] The stable operator group S is a group consisting of all stable operators, each of which is a codeword vector is an operator with a +1 eigenvalue for the codeword vector The composition is as follows: Mathematical formula 7.

[0351]

[0352] The stable operator group S of the [[n,k,d]] quantum error correction code can be expressed as in the following mathematical expression 8 by the stabilizer generator.

[0353]

[0354] EPR status The stable operator group is as shown in mathematical expression 9 below.

[0355]

[0356] An operator that is commutative with a safe operator is called a normalizer, and in quantum error-correcting codes, a logical operator is defined by utilizing an operator (N(S) / S) that is not a stable operator among the normal operators. The minimum distance d of a quantum error-correcting code is defined through the smallest weight among the logical operators. Here, the weight is the number of Pauli operators other than I included in the operator.

[0357] d min Quantum error correction codes with distance are identical to classical error correction codes in that they Errors with weight can be corrected, d min -1 error can always detect whether an error has occurred.

[0358] For example, the stable operator constructor and logical X, Z operators of the quantum error correction code [[7,1,3]] are as follows in mathematical expression 10.

[0359]

[0360] FIG. 23 is a diagram illustrating an example of an encoding circuit and a safe operator syndrome measurement circuit in a system applicable to the present disclosure. Specifically, (a) of FIG. 23 shows an encoding circuit of a [[7,1,3]] quantum error correction code, and (b) of FIG. 23 shows a XIIXIXX safe operator syndrome measurement circuit.

[0361] In quantum error correction codes, measurements are performed based on stability operators for error detection, and errors are corrected according to the measurement result syndrome. The error E after the measurement is the stability operator S. i If the anti-commute law holds, S i The measurement result will be -1, and if exchanged (commuted), it will be +1.

[0362] Unlike digital error-correcting codes, quantum error-correcting codes exhibit degeneracy. Therefore, different correctable errors can have the same syndrome value. Such codes are called degenerate codes. This degeneracy opens up the possibility that the performance of quantum error-correcting codes could surpass the limitations of classical error-correcting codes.

[0363]

[0364] In the field of quantum communication, a syndrome refers to a measurement result obtained from a set of qubits used to detect and correct errors in a quantum system. The goal of quantum error correction is to protect quantum information from errors that may arise due to noise and other environmental factors. To achieve this, a set of error-correcting codes are used to detect and correct errors that occur during quantum operations. When an error occurs in a quantum system, it can affect one or more qubits. To detect which qubits are affected by an error, a series of measurements are performed on the quantum system. These measurements generate a set of syndrome values, which can be used to identify the location and nature of the error. The syndrome values ​​can then be used to determine the corrective action that needs to be applied to the qubits to restore the original quantum information. Using quantum error-correcting codes and syndromes, the impact of errors on quantum communication and computation can be significantly reduced.

[0365]

[0366] Error Digitization

[0367] Quantum errors occur continuously. However, by introducing stability operator measurements during the error correction process, continuous errors can be corrected through discontinuous Pauli error correction. Any 1-qubit unitary operator U can be expressed as follows: Equation 11. After stability operator measurements, the U error collapses into one of I, X, Y, or Z.

[0368]

[0369] At this time, is a complex number, Satisfies.

[0370] Therefore, after the stability operator measurement, the error remains in the form of one of the Pauli errors discretely occurring in the codeword, and when the quantum error correction code correctly corrects the error, the error is corrected by performing a correcting operation that multiplies the collapsed Pauli error or the Pauli error by the stability operator form. The correcting operation can be physically performed, and in some cases, it is replaced by recording it in SW instead of physically performing it.

[0371] Threshold

[0372] Error rate of codeword after error correction code encoding (p L ) The error rate (p) of the physical qubit before this encoding process phy ) requires a certain physical error rate to be lower than that.

[0373]

[0374] The corresponding physical error rate is defined as a threshold, and in circuit-based quantum error correction codes (QECCs), the logical error rate, which is the error rate of the codeword, decreases exponentially through concatenation, and in topological codes (Topological QECCs), the logical error rate decreases exponentially as the code distance increases. The logical error rate includes the fault-tolerant logical operation error rate. The threshold value has different values ​​depending on the type of quantum error correction code and the error occurrence channel modeling, and depending on the code, topological codes show a lower threshold than concatenated-based codes. Pseudothresholds are also utilized depending on the channel modeling. Table 5 below shows the threshold estimates.

[0375] CodeThresholdGeometric Constraints[[7,1,3]]O(10 -4 -10 -6 )Arbitrary Interaction[[7,1,3]]O(10 -5 )2D Nearest Neighbor array[[23,1,7]]O(10 -3 )Arbitrary InteractionSurface CodeO(10 -2 -10 -3 )2D Nearest Neighbor array

[0376]

[0377] Quantum Entanglement Distillation Protocol (EDP)

[0378] Quantum entanglement distillation protocol is a technique that utilizes multiple entangled states with low fidelity, local operations, and classical communication (LOCC) to share a small number of entangled states with high fidelity among multiple parties.

[0379] Various entanglement distillation protocols have been developed, including recurrence, QPA, breeding, and hashing. Among them, recurrence and QPA protocols utilize classical communication bidirectionally, from Alice to Bob and Bob to Alice, while breeding and hashing protocols utilize classical communication unidirectionally, from Alice to Bob. In general, two-way protocols have the advantage of operating in poor channel conditions compared to unidirectional protocols, and unidirectional protocols utilize fewer resources than bidirectional protocols.

[0380] High-fidelity EPR states or entangled states generated by the entanglement distillation protocol can be utilized in quantum teleportation or quantum direct communication, quantum key distribution, and distributed quantum computing.

[0381]

[0382] QPA protocol

[0383] The QPA protocol is a bidirectional protocol that utilizes two EPR states in each round to probabilistically generate one EPR state with high fidelity.

[0384] When each particle in the EPR state is shared between the sender and receiver, as a first step, Alice and Bob operate the unitary operator as shown in Equation 13 below on the individual qubits of each EPR pair.

[0385]

[0386] The Unitary course is and are mutually converted. That is, This is because the operations performed thereafter are good at correcting Y and Z errors, but not good at correcting X errors, so the Z errors in the state after each round are converted to Y errors and corrected in the subsequent rounds.

[0387] In the second step, Alice and Bob perform the CNOT operator between the two EPR pairs, and finally measure the EPR state that acted as the target in the Z basis and share the measurement value through classical communication. If the shared measurement value is the same for Alice and Bob, the EPR state acted as the control qubit is judged to have higher fidelity and the corresponding entangled state is used thereafter. If the shared measurement value is different, the EPR state acted as the control qubit is discarded. Therefore, the QPA protocol is probabilistically successful (preserving the EPR state acted as the control qubit) depending on the measurement result, and the success probability (p succ ) is the initial fidelity (F or F init ) is determined by.

[0388] When the initial EPR state passes through the Symmetric Depolarizing Channel, the entangled state after the channel can be expressed as in Equation 14 below.

[0389]

[0390] Werner state of fidelity F (W F ) when performing a single round, the success probability is as follows: Equation 15.

[0391]

[0392] FIG. 24 is a diagram illustrating an example of the success probability of the QPA protocol in a system applicable to the present disclosure.

[0393]

[0394] FIG. 25 is a diagram illustrating an example of a process of performing a QPA protocol in a system applicable to the present disclosure.

[0395] p succ The entangled state generated with the probability of is as follows: Equation 16.

[0396]

[0397]

[0398] FIG. 26 is a diagram illustrating an example of the fidelity performance of the QPA protocol in a system applicable to the present disclosure.

[0399] Additionally, to achieve high fidelity, each round can be performed repeatedly, with at least 2 rounds performed when n rounds are performed. n The dog's EPR state is utilized.

[0400] Depending on the availability of memory in the QPA protocol, each round of the QPA protocol can be connected serially rather than in parallel and operated in pumping mode. When operating in entanglement pumping mode, instead of proceeding a new round using the EPR state output from each round, the round is performed using the output EPR state of one round and the initial EPR state, which has the advantage of reducing the number of quantum memories used. However, the increase in fidelity is low, and when fidelity saturates, it must be replaced with a round output EPR pair.

[0401]

[0402] Recurrence protocol

[0403] The Recurrence protocol is a bidirectional protocol similar to the QPA protocol, which utilizes two EPR states in each round to probabilistically generate one EPR state with high fidelity. Unlike the QPA protocol, it is different from the U protocol in the first step. A , U BPerform a non-unitary twirling operation corresponding to . In the first step, Alice and Bob probabilistically apply the twirling operator to individual qubits of each EPR pair. Equation 17 represents the twirling operation.

[0404]

[0405] The twirling process transforms an arbitrary mixed state M into a Werner state W F This is the process for changing to .

[0406] In the second step, Alice and Bob perform the CNOT operator between the two EPR pairs, and finally measure the EPR state that acted as the target in the Z basis and share the measurement value through classical communication. The shared measurement value determines that the EPR state acted as the control qubit has higher fidelity and is used thereafter. If the shared measurement value is different, the EPR state acted as the control qubit is discarded. Therefore, the recurrence protocol is probabilistically successful depending on the measurement result, and the success probability (p succ ) is determined by the initial fidelity. When a single round is performed using the Werner state of fidelity F, the success probability is as follows: Equation 18.

[0407]

[0408] Additionally, to achieve high fidelity, each round can be performed repeatedly, with at least 2 rounds performed when n rounds are performed. n The dog's EPR state is utilized.

[0409] When a single round is performed using the Werner state of fidelity F, the output fidelity F' is as follows: Equation 19.

[0410]

[0411] Depending on the availability of memory in the recurrence protocol, each round of the recurrence protocol can be connected serially rather than in parallel and operated in pumping mode. When operating in entanglement pumping mode, instead of proceeding a new round using the EPR state output from each round, the round is performed using the output EPR state of one round and the initial EPR state, which has the advantage of reducing the use of quantum memory. However, the increase in fidelity is low, and when fidelity saturates, it must be replaced with a round output EPR pair.

[0412] Due to the difference in operators used in the first step, it has lower fidelity performance than the QPA protocol when an arbitrary state is used as the input state.

[0413]

[0414] Hashing protocol

[0415] FIG. 27 is a diagram illustrating an example of a QECCs-based hashing protocol in a system applicable to the present disclosure.

[0416] Hashing protocol is a one-way protocol that uses the decoding circuit of quantum error correction code to distill into an entangled state. In the first step, Alice and Bob encode and decode quantum error correction codes using the circuit (U t enc ,U dec) is performed on each EPR state, and a specific qubit is measured, similar to syndrome extraction. In the second step, Alice shares the measurement results with Bob, and Bob performs a correction operator according to the product pattern of his measurement results and Alice's measurement results. The correction operator is determined by the correction operator according to the syndrome of the quantum error correction code, and in the Hashing protocol, the syndrome is obtained as the product of the measurement results. If the initial fidelity is greater than a threshold value, the unmeasured EPR state has improved fidelity.

[0417] For the EPR state, since it is a CSS state and an H-invariant state (CSS-H invariant state) that is closed for the H transformation of the fixed operator, a decryption circuit of an arbitrary error correction code is utilized. Since it is a CSS state like the 3-qubit GHZ state utilized in multi-party, but is an H-variable state (CSS-H variant), a decryption circuit of a CSS quantum error correction code must be utilized, and for the non-CSS state, a CSS-H code, that is, a quantum error-definite code in which the logical H is expressed in the form of a tensor product of individual (transversal) H, must be utilized.

[0418] CSS states are fixed by each operator S i ∈S means a state that can be expressed by one of the Pauli operators (X or Y or Z).

[0419]

[0420] Breeding Protocol

[0421] The Breeding protocol is a one-way protocol, similar to the Hashing protocol. In the Breeding protocol, This is a protocol that improves n>m low-fidelity EPR states to high-fidelity by utilizing m pure entangled states when Alice and Bob share a pure entangled state. In this case, to distill one EPR state, Von-Neumann entropy A sufficient number of pure EPR states are required, and theoretically, the entropy of all n EPR states can be removed to create a pure entangled state.

[0422] To perform the Breeding protocol, first, Alice and Bob perform CNOT operations on n impure EPR states and m pure EPR states for a specific BXOR detection process, and then measure m pure EPR states. Second, Alice shares the measurement results with Bob, and Bob performs a correction operator according to the product pattern of his measurement results and Alice's measurement results. If the initial fidelity is greater than a threshold value, the unmeasured EPR states have a fidelity of 1.

[0423] The fidelity performance of the protocol output value can be determined depending on how the BXOR process is performed, and the BXOR circuit can be determined based on the decoding circuit of the quantum error correction code in the BXOR detection process.

[0424] The Hashing protocol and Breeding protocol described above have a higher Yield (the number of EPR states required to obtain one EPR state with Fidelity 1) than QPA or Recurrence, and the Yield is expressed as in the following mathematical expression 20.

[0425]

[0426] Breeding / Hashing protocols can be utilized in conjunction with QPA / Recurrence as needed.

[0427]

[0428] Unidirectional or bidirectional distinction of the Entanglement Distillation Protocol (EDP)

[0429] The bidirectional (Recurrence or QPA protocol) or unidirectional EDP technique is composed of a transmitter and receiver, a quantum channel, and a classical channel, as shown in Fig. 28 and Fig. 30.

[0430] (1) Bidirectional EDP technique

[0431] Figure 28 is a diagram illustrating an example of the structure of a single round of a bidirectional EDP technique in a system applicable to the present disclosure. Specifically, Figure 31 illustrates the basic structure of a single round of an existing bidirectional EDP technique.

[0432] In a single round, the bidirectional EDP technique works as follows:

[0433] ① The sender (Alice) uses nonlinear elements to create two EPR states.

[0434] ② The sender transmits one qubit from each EPR state to the receiver (Bob) (2 in total). At this time, the quantum channel includes the generation defect of the EPR state, the wired and wireless photonic channel, and the quantum memory error channel.

[0435] ③ The sender and receiver perform a unitary operation by grouping two EPR states. If the Werner state is not generated after the quantum channel, the Werner state W F To convert to , unitary operation (U) and CNOT operation are performed. On the other hand, if a Werner state is generated after the quantum channel, only CNOT operation is performed.

[0436] ④ The sender and receiver measure some qubits. ③ The qubit used as the target qubit for the CNOT operation is measured. Figure 29 is a diagram illustrating an example of the process by which the sender and receiver measure qubits in a system applicable to the present disclosure.

[0437] ⑤ The receiver transmits its measurement result (1 bit) to the sender, and the sender transmits its measurement result (1 bit) to the receiver through the Classical Channel.

[0438] ⑥ The sender and receiver compare their own measurement result bits with the other party's measurement result bits received via the Classical Channel. If the shared measurement results do not match, the unused EPR states are discarded and steps ① through ⑤ are repeated. If the measurement results match, the corresponding qubits are preserved.

[0439] If an EPR state with higher fidelity is required, processes ② to ⑥ are performed using the preserved qubits in a single round. At least 2 are performed when n rounds are performed. n The EPR state must be shared between the sender and receiver, and additional qubits are required depending on the measurement results. Ultimately, a single high-fidelity EPR state is generated.

[0440]

[0441] (2) One-way EDP technique

[0442] FIG. 30 is a diagram illustrating an example of the structure of a unidirectional EDP technique in a system applicable to the present disclosure.

[0443] ① In the case of a one-way EDP utilizing a [[n,k,d]] quantum error correction code, the sender (Alice) uses a nonlinear element to generate n EPR states.

[0444] ② The sender transmits one qubit from each EPR state to the receiver (Bob) (n in total). At this time, the quantum channel includes the generation defect of the EPR state, wired and wireless photonic channels, and quantum memory error channels.

[0445] ③ The sender and receiver perform a unitary operation, and in the case of a one-way EDP, they perform a circuit or a BXOR circuit related to decoding the promised [[n,k,d]] quantum error correction code.

[0446] ④ The sender and receiver each measure (nk) qubits. At this time, the location of the measured qubit is determined based on the characteristics of the BXOR circuit or the [[n,k,d]] quantum error correction code.

[0447] ⑤ The receiver transmits the measurement result ((nk) bits) to the sender through the Classical Channel.

[0448] ⑥ The sender estimates the syndrome or error from the receiver's measurement results and performs a correction operation to correct the error on the qubits (k) that were not measured in ④. The correction operator is determined based on the product pattern of the measurement results, and one of the Pauli operators I, X, Y, or Z operates on the sender's qubit among a pair of EPR states.

[0449]

[0450] Finally, for Recurrence or QPA bidirectional EDP, one high-fidelity EPR state is preserved, and for unidirectional EDP using [[n,k,d]] error-correcting codes, k high-fidelity EPR states are preserved.

[0451] Quantum entanglement distillation can correct channel errors in the entangled state shared by Alice and Bob through the above process. This occurs by transferring a portion of the entanglement from multiple entangled states to a smaller number of entangled states, utilizing classical channels.

[0452] Thus, EDP aims to create entangled states with high fidelity. Currently known QPA / Recurrence protocols have the limitation of requiring significant resources to generate the desired fidelity, while hashing / breeding protocols based on quantum error-correcting codes operate only at high initial fidelity.

[0453]

[0454] EDP ​​protocol in adaptive mode

[0455] FIG. 31 is a diagram illustrating an example of the structure of an adaptive mode EDP technique in a system applicable to the present disclosure. Specifically, FIG. 34 illustrates the basic structure of an adaptive mode-based EDP technique.

[0456]

[0457] In order to reduce the resources required for the existing QPA technique and to expand the fidelity range in which the error correction code-based EDP operates, in step ③, the decoding circuit of the quantum error correction code or the QPA circuit is selectively utilized, but in step ⑤, the method of Fig. 34 is applied, which performs a process of discarding some syndrome patterns of QECCs similar to the bidirectional EDP.

[0458] FIG. 32 is a drawing illustrating an example of a block diagram of a bidirectional EDP technique based on quantum error correcting codes (QECCs) in a system applicable to the present disclosure.

[0459] The process of the EDP technique based on Adaptive mode QECCs proposed in this disclosure is as follows.

[0460] The process of the adaptive mode-based EDP technique is as follows. First, when the QPA mode is selected as the adaptive mode, the bidirectional EDP protocol of Fig. 31 is performed. The following describes the process of the QECCs-based bidirectional EDP technique as the adaptive mode, and the process of dividing the QPA mode and the QECCs-based bidirectional EDP mode is described in [Fidelity-based QECCs parameter estimation process].

[0461] ① When using the [[n,k,d]] quantum error correction code, the sender (Alice) generates n EPR states using nonlinear elements.

[0462] ② The sender transmits one qubit from each EPR state to the receiver (Bob). At this time, the quantum channel includes the generation defect of the EPR state, a wired / wireless photonic channel, and a quantum memory error channel.

[0463] ③ The sender and receiver perform the decoding related circuit of the [[n,k,d]] quantum error correction code promised for the Unitary Operation operation as a unitary operation.

[0464] ④ The sender and receiver each measure (nk) qubits. At this time, the location of the measured qubit is determined based on the characteristics of the [[n,k,d]] quantum error correction code.

[0465] ⑤ The receiver and the sender transmit the measurement results to each other. At this time, each (nk) bits is transmitted through the Classical Channel.

[0466] ⑥ The sender and receiver multiply their own measurement results with the measurement results of the other party, and according to the pattern of the multiplied values, perform a correction operation on the k EPR states that were not measured in step ④, and then preserve or discard the EPR states. If the EPR states are discarded, steps ① to ⑤ are performed again. When s1(∈{-1,1}) is Alice's measurement result and s2(∈{-1,1}) is Bob's measurement result, the product of the measurement results ((-1) s' If (s1s2) belongs to the promised pattern S, the sender performs a correction operation on his qubit among the unmeasured EPR states (k). At this time, the probability of performing the correction operation is p succ is defined as . The correction operation is determined as one of I, X, Y, and Z according to the pattern of s', and operates on the sender's qubit among the EPR pair states of the unmeasured EPR states.

[0467] Variable d that determines pattern S corr ( <d)은 [[n,k,d]] 양자 오류정정 부호를 통해 정정할 수 있는 Errors to be corrected in EDP using QECCs in adaptive mode among the errors (t) corr ) and is agreed upon between the sender and receiver in advance. Errors to be corrected (t corr ) is defined as in the following mathematical expression 21.

[0468]

[0469] For example, using the quantum error correction code [[7,1,3]], t corr If =1, the promised pattern S is generated as the union of the syndrome patterns in which no error occurred and the syndrome patterns in which one error occurred. If it has a stable operator, S is generated as follows:

[0470]

[0471] After correcting the EPR state through an appropriate Pauli operation according to the syndrome, the EPR state is preserved, and if a syndrome is generated, i.e., a product of measurement results other than S, the unmeasured EPR state is discarded.

[0472]

[0473] When [[7,1,3]],[[5,1,3]] QECCs are used in adaptive mode, the fidelity (F') performance and p_succ performance are as shown in Fig. 33, Fig. 34, Fig. 35, and Fig. 36. In the case of [[7,1,3]] quantum error correction code, 2 7-1 =64 syndrome patterns exist, t corr If the value of is 1, errors are corrected for 22 patterns (|S|=22), and if other patterns are measured, the EPR state is discarded.

[0474]

[0475] FIG. 33 is a diagram illustrating an example of the fidelity performance of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0476] Figure 33 shows the EDP performance when [[7,1,3]] QECCs are used in two-way.

[0477] Figure 33 shows the fidelity performance of the output EPR state when utilizing [[7,1,3]] QECCs. In Figure 33, the line of [[7,1,3]] QECCs is related to the case where all syndromes are corrected in [[7,1,3]] QECCs. In Figure 33, [[7,1,3]] t corr The line of =1 corresponds to the case where the EPR state is preserved after error correction for syndrome patterns in which errors are estimated to be 0 or 1, and the EPR state is discarded for other syndrome patterns. In Fig. 33, [[7,1,3]] t corrThe line of =0 corresponds to the case where the EPR state is preserved for syndrome patterns in which no error occurs, and the EPR state is discarded for other syndrome patterns.

[0478] Referring to Figure 33, the dotted line from (initial fidelity 0.6, output fidelity 0.6) to (initial fidelity 1, output fidelity 1) corresponds to the graph of y=x. That is, the dotted line represents the case where F'=F, and it is effective to perform EDP at an initial fidelity located above the dotted line. For example, when utilizing [[7,1,3]] QECCs, it is efficient to operate at an initial fidelity of 0.92 or higher.

[0479] Adaptive mode EDP based on QECCs is used in harsh environments with low initial fidelity. corr If =0, then t corr =1 may work better than the other case.

[0480] In the adaptive mode EDP based on QECCs, the sender and receiver each measure (nk) qubits among n EPR states, and then derive t from the product of the measurement results (i.e., syndrome). corr If an excessive number of errors occur, for example, t corr =0 and more than 0 errors occurred, or, for example, t corr =1 and if more than 1 error occurs, discard the k unmeasured qubits.

[0481] In the adaptive mode EDP based on QECCs, the sender and receiver each measure (nk) qubits among n EPR states, and then derive t from the product of the measurement results (i.e., syndrome). corr less than or equal to t corr If a number of errors occur, for example, t corr =0 and 0 errors occurred, or, for example, t corr=1 and if 1 or 0 errors occur, the k unmeasured qubits are corrected and preserved.

[0482] [[7,1,3]] t corr =0 creates a syndrome by sharing the product of the measurement results from the measured EPR states, and then preserves the remaining unmeasured EPR states only if no error occurs in the interpretation of the syndrome. That is, t corr If an error exceeding 1 occurs, the remaining unmeasured EPR states are discarded. [[7,1,3]] t corr =0 is easy to use in harsh environments with low initial fidelity, and as shown in Fig. 39, it shows good performance in all initial fidelities. That is, in the graph of Fig. 39, [[7,1,3]] t corr =0 is located above the y=x graph in all initial fidelity regions.

[0483] [[7,1,3]] t corr =1 creates a syndrome by sharing the product of the measurement results, and when the interpretation of the syndrome results in two or more errors, the remaining unmeasured EPR states are discarded. When the number of errors is 0 or 1, the remaining unmeasured EPR states are preserved after error correction. [[7,1,3]] t corr =1 intersects the graph of y=x near (0.73, 0.73). Therefore, at that intersection point, i.e., at an initial fidelity greater than 0.73, [[7,1,3]] t corr =1 indicates good performance.

[0484] [[7,1,3]] QECCs is a protocol that preserves the remaining unmeasured EPR states for all syndromes as a result of syndrome interpretation. That is, [[7,1,3]] QECCs shows good performance even in better environments, i.e., environments with initial fidelity higher than 0.94.

[0485]

[0486] FIG. 34 is a diagram illustrating an example of the success probability performance of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0487] Figure 34 shows the EDP performance when [[7,1,3]] QECCs are used in two ways. Figure 34 also shows the probability of preserving the output EPR state when [[7,1,3]] QECCs are used.

[0488] In Fig. 33, the line of [[7,1,3]] QECCs remains 1 because it preserves the EPR state for all syndrome patterns. In Fig. 33, [[7,1,3]] t corr The line =1 represents the probability of preserving the EPR state after correction for errors with 0 or 1 syndrome pattern. In Fig. 33, [[7,1,3]] t corr The line of =0 is the probability of preserving the EPR state for syndrome patterns in which no errors occur.

[0489] Referring to Figure 34, the y-axis represents the success probability Psucc. Success probability P succ corresponds to the probability of preserving the remaining unmeasured EPR states without discarding them.

[0490] [[7,1,3]] QECCs do not discard any EPR states, so the success rate for the remaining EPR states is 1.

[0491] [[7,1,3]] t corr =1 creates a syndrome by sharing the product of the measurement results, and when the syndrome interpretation results in two or more errors, the remaining unmeasured EPR states are discarded. When the number of errors is 0 or 1, error correction is performed on the remaining unmeasured EPR states and then they are preserved.

[0492] [[7,1,3]] t corr=0 creates a syndrome by sharing the product of the measurement results from the measured EPR states, and then preserves the remaining unmeasured EPR states only if no error occurs in the interpretation of the syndrome. That is, t corr If an error exceeding 1 occurs, the remaining unmeasured EPR states are discarded.

[0493]

[0494] FIG. 35 is a diagram illustrating an example of the fidelity performance of an adaptive mode EDP based on QECCs in a system applicable to the present disclosure.

[0495] Fidelity is closely related to the channel's error rate. Lower fidelity corresponds to a channel environment with a higher error rate.

[0496] Figure 35 shows the fidelity when QECCs are [[5,1,3]].

[0497] Referring to Figure 35, the dotted line extending from (initial fidelity 0.6, output fidelity 0.6) to (initial fidelity 1, output fidelity 1) corresponds to the graph of y=x.

[0498] [[5,1,3]] t corr =0 creates a syndrome by sharing the product of the measurement results from the measured EPR states, and then preserves the remaining unmeasured EPR states only if no error occurs in the interpretation of the syndrome. That is, t corr If an error exceeding 1 occurs, the remaining unmeasured EPR states are discarded. [[5,1,3]] t corr =0 is easy to use in harsh environments with low initial fidelity, and as shown in Fig. 38, it shows good performance in all initial fidelities. That is, in the graph of Fig. 38, [[5,1,3]] t corr =0 is located above the y=x graph in all initial fidelity regions.

[0499] [[5,1,3]] QECCs are protocols that preserve the remaining unmeasured EPR states for all syndromes as a result of the syndrome interpretation. That is, [[5,1,3]] QECCs can be used even in better environments, i.e., environments with an initial fidelity of 0.85 or higher.

[0500]

[0501] FIG. 36 is a diagram illustrating an example of the success probability performance of an adaptive mode EDP based on QECCs in a system applicable to the present disclosure.

[0502] Figure 36 shows the success probability when QECCs are [[5,1,3]].

[0503] Referring to Figure 36, the y-axis represents the success probability Psucc. The success probability Psucc corresponds to the probability of preserving the remaining unmeasured EPR states without discarding them.

[0504] [[5,1,3]] QECCs do not discard any EPR states, so the success rate for the remaining EPR states is 1.

[0505] [[5,1,3]] t corr =0 creates a syndrome by sharing the product of the measurement results from the measured EPR states, and then preserves the remaining unmeasured EPR states only if no error occurs in the interpretation of the syndrome. That is, t corr If an error exceeding 1 occurs, the remaining unmeasured EPR states are discarded.

[0506] The above-described Figures 33 and 34 and Figures 35 and 36 show the EDP performance when [[7,1,3]] and [[5,1,3]] QECCs are used in two-way, respectively. Figure 34 shows the probability of preserving the output EPR state when [[7,1,3]] QECCs are used. In the case of the line corresponding to [[7,1,3]] QECCs, it remains 1 because the EPR state is preserved for all syndrome patterns. In Figure 34, [[7,1,3]] t corr For the line corresponding to =1, it is the probability of preserving the EPR state after correction for an error with 0 or 1 syndrome pattern. [[7,1,3]] t corr For the line corresponding to =0, it is the probability of preserving the EPR state for a syndrome pattern in which no error occurs. In the case of Figures 35 and 36, it corresponds to the fidelity and success probability when QECCs are [[5,1,3]].

[0507] The methods described in this disclosure for preparing EPR pairs at the sender (Alice) and transmitting one qubit from each EPR pair to the receiver (Bob) are described uniformly for convenience of explanation. However, it is self-evident that the same method can be applied to a method in which an EPR pair is prepared at a third-party node and transmitted to the first node (Alice) and the second node (Bob) to perform all of the methods described above.

[0508]

[0509] Average fidelity-based parameter estimation process

[0510] When using QECCs in adaptive mode, the fidelity of the EPR state generated after the EDP process is the target fidelity (F target ) to utilize the minimum EPR state (fidelity F) while satisfying the parameters n, k, d of QECCs and parameter d of adaptive mode. corr or t corrTo estimate , an optimization process is performed according to the following mathematical expression 22.

[0511]

[0512] The number of qubits utilized and the final fidelity after n rounds of the QPA protocol are determined as shown in Equation 23 below.

[0513]

[0514] At this time p succ,i ,F i ' represent the success probability of the ith round and the fidelity after the ith round, respectively.

[0515] When QECCs are used in adaptive mode, the following mathematical expression 24 is used.

[0516]

[0517] and p succ and has a bound as shown in mathematical expression 25 below.

[0518]

[0519] [[7,1,3]],[[5,1,3]] Actual fidelity (F') performance when QECCs are used in adaptive mode, p succ Performance estimation (Est) based on performance and bound is as shown in Figs. 37 to 40.

[0520] FIG. 37 is a diagram illustrating an example of fidelity estimation of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0521] FIG. 38 is a diagram illustrating an example of fidelity estimation of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0522] FIG. 39 is a diagram illustrating an example of estimation of the success probability of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0523] FIG. 40 is a diagram illustrating an example of estimation of the success probability of QECCs-based adaptive mode EDP in a system applicable to the present disclosure.

[0524] Figure 37 shows the estimated performance when using [[7,1,3]] QECCs. Figure 38 shows the estimated performance when using [[5,1,3]] QECCs. In Figure 37, Est [[7,1,3]] t corr =1 or Est [[7,1,3]] t corr =0 means an estimate of fidelity, [[7,1,3]] t corr =1 or [[7,1,3]] t corr =0 means the actual value of fidelity.

[0525] Est [[7,1,3]] t corr =1 or Est [[5,1,3]] t corr In the case of =1, the EPR state is preserved after correction for syndrome patterns that are estimated to have 0 or 1 errors, and in other cases, the EPR state is discarded. Est [[7,1,3]] t corr =0 or Est [[5,1,3]] t corr =0 corresponds to the case where the EPR state is preserved only for syndrome patterns in which no error occurred, and the EPR state is discarded in other cases.

[0526]

[0527] Referring to FIGS. 37 to 40, Est [[7,1,3]] t corr =1 or Est [[5,1,3]] t corr All values ​​with Est, such as =1, are estimation values. For example, Est [[7,1,3]] t in Figures 37 and 38 corr =1 or Est [[5,1,3]] t corr=1 means the estimated value of the fidelity, i.e., the output fidelity (output Fidelity F'). Also, in Fig. 39 and Fig. 40, Est [[7,1,3]] t corr =1 or Est [[5,1,3]] t corr =1 means the estimated value of the success probability (success probability Psucc).

[0528] Referring to FIGS. 37 to 40, [[7,1,3]] t corr =1 or [[5,1,3]] t corr =1, all values ​​without Est represent actual simulation values. In the case of [[7,1,3]] (or [[5,1,3]]), it represents the value of fidelity, that is, output fidelity (output Fidelity F'), from the result of actually performing the simulation using the quantum error correction code of [[7,1,3]] (or [[5,1,3]]). Therefore, in order not to overestimate the performance of Fidelity, the estimated value was designed so that the estimated value < the actual value. In addition, in order not to overestimate the performance of the success probability (Psucc), the estimated value was designed so that the estimated value < the actual value.

[0529] [[7,1,3]] and [[5,1,3]] are different error-correcting codes. In the case of the [[5,1,3]] code, 5 qubits are used to encode one logical information, and since d=3, (d-1) / 2=1 all errors can be corrected. There is no case in which 2 errors are estimated in the [[5,1,3]] code. In the case of the [[7,1,3]] code, 7 qubits are used to encode one logical information, and since d=3, (d-1) / 2=1 all errors can be corrected. However, unlike the [[5,1,3]] code, the [[7,1,3]] code can additionally correct some 2 errors. Therefore, compared to FIGS. 38 and 40, in FIGS. 37 and 39, tcorr =1 The graph is additionally shown. That is, in Fig. 37 and Fig. 39, it is shown for the case of discarding a syndrome in which some two errors occurred.

[0530] Tables 6 through 8 below show specific goal fidelity (F target =1-10 -6 or 1-10 -7 ) is the result of the optimal mode and parameter estimation to satisfy the condition. The mode is determined among the adaptive modes QPA, QECCs (QECCs-based bidirectional EDP technique), and Detecting, and means the EDP operation method that utilizes the minimum resources in each initial fidelity. 'QPA' is QPA mode, and 'QECCs' is QECCs-based bidirectional EDP protocol. corr If ≥1, 'Detecting' is t in a bidirectional EDP protocol based on QECCs. corr =0. Num_EPR is the number of EPR states utilized in each mode. n,k,d,t corr When a bidirectional protocol based on QECCs is utilized in mode, the optimal code and number of corrected errors (t) corr ) and is an estimated value using bound. Gain is the number of qubits reduced when a bidirectional protocol based on QECCs is used compared to QPA, and QPA round is the number of rounds when used in QPA mode.

[0531]

[0532] Table 6 is F target =1-10 -6 It corresponds to the EDP protocol and spec (0.85≤F≤0.99).

[0533] Table 7 is F target =1-10 -7 It corresponds to the optimal EDP protocol and spec in .

[0534] Table 8 is Ftarget =1-10 -6 It corresponds to the EDP protocol and spec (0.999≤F≤0.9999).

[0535]

[0536] F1-F target modeNum_EPRnkdt corr GainQPA Round0.851.00E-06'QPA'792.9544'-''-''-''-''-'60.861.00E-06'QPA'654.7041'-''-''- ''-''-'60.871.00E-06'QPA'542.753'-''-''-''-''-'60.881.00E-06'QPA'156.4529'-''-' '-''-''-'50.891.00E-06'QPA'135.3346'-''-''-''-''-'50.91.00E-06'QPA'117.3719'-'' -''-''-''-'50.911.00E-06'QPA'102.049'-''-''-''-''-'50.921.00E-06'QPA'88.9412'-' '-''-''-''-'50.931.00E-06'QECCs'74.7453291111-2.9528'-'0.941.00E-06'Detecting'4 7.839927390-20.1893'-'0.951.00E-06'QECCs'23.797282101-35.8961'-'0.961.00E-06'QE CCs'20.2648282101-32.2237'-'0.971.00E-06'QECCs'11.162427391-10.1298'-'0.981.00E -06'Detecting'8.285525570-11.047'-'0.991.00E-06'Detecting'4.697816450-3.8568'-'

[0537]

[0538] F1-F target modeNum_EPRnkdt corrGainQPA Round0.851.00E-07'QPA'792.9544'-''-''-''-''-'60.861.00E-07'QPA'654.7041'-''-''-''-''-'60.871.00E-07'QPA'542.753'-''-''-''-''-'60.881.00E-07'QPA'451.691'-''-''-''-''-'60.891.00E-07'QPA'377.3025'-''-''-''-''-'60.91.00E-07'QPA'316.2845'-''-''-''-''-'60.911.00E-07'QPA'102.049'-''-''-''-''-'50.921.00E-07'QPA'88.9412'-''-''-''-''-'50.931.00E-07'QPA'77.6981'-''-''-''-''-'50.941.00E-07'QECCs'61.1904291111-6.8388'-'0.951.00E-07'QECCs'50.8073291111-8.8858'-'0.961.00E-07'QECCs'20.2648282101-32.2237'-'0.971.00E-07'QECCs'17.6041282101-28.6431'-'0.981.00E-07'QECCs'10.01227391-30.816'-'0.991.00E-07'Detecting'5.186921560-12.3894'-'

[0539]

[0540] F1-F target modeNum_EPRnkdt corrGainQPA Round0.9991.00E-06'Detecting'1.921317940-2.0947'-'0.99911.00E-06'Detecting'1.91817940-2.0964'-'0.99921.00E-06'D etecting'1.914817940-2.098'-'0.99931.00E-06'Detecting'1.911517940-2.0997'-'0.99941.00E-06'QECCs'1.8235311752-2.1 861'-'0.99951.00E-06'QECCs'1.8235311752-2.1845'-'0.99961.00E-06'QECCs'1.8235311752-2.1829'-'0.99971.00E-06'QECCs '1.8235311752-2.1813'-'0.99981.00E-06'QECCs'1.8235311752-2.1797'-'0.99991.00E-06'Detecting'1.669215930-2.3324'-'

[0541]

[0542] The method of preparing an EPR state at a sender (Alice) and transmitting one qubit from each EPR state from the sender to a receiver (Bob) described in this disclosure are described uniformly for convenience of explanation. However, it is self-evident that the same can be applied to a method of performing all the methods described above by preparing an EPR state at a third-party node and transmitting it to the first node (Alice) and the second node (Bob), or all the methods of preparing an EPR state at a receiver and transmitting it to the first node (Alice).

[0543]

[0544] Additionally, depending on the environment, the parameter estimation process can be performed in one of the following scenarios ①, ②, and ③.

[0545] ① When the base station and terminal share a table based on initial fidelity (e.g., Table 6, Table 7, Table 8, etc. are shared in advance)

[0546] ② In an environment where the table is not shared, when the base station and terminal each perform parameter estimation according to the initial fidelity

[0547] ③ In an environment where the table is not shared, when the base station performs parameter estimation and informs the terminal of the parameter values.

[0548] In case ①, the initial fidelity (F) measured by the base station and the terminal init ) is not in the pre-shared Table, the initial fidelity is lower or equal (F≤F init ) and utilize the parameters. For example, if the base station and the terminal share Table 6, but the measured initial fidelity is 0.935, by utilizing the parameters of n=29, k=1, d=11, t_corr=1 with the initial fidelity of 0.93, F target The Look-up Table corresponding to the optimal EDP protocol and spec exemplified here is the initial fidelity (F), target fidelity F target It can be configured individually according to the method, and it is assumed that the base station and the terminal both have individual look-up tables in a pre-arranged manner.

[0549] In scenarios ② and ③, the proposed technique is used based on the measured initial fidelity to calculate n, k, d, t. corr The parameters can be estimated. The initial fidelity is measured as F=0.96, and the target fidelity is 1-10 -7 In case ②, the base station and the terminal perform the optimization function so that n=28, k=2, d=10, t corr =1 values ​​are derived respectively and the quantum error correction code decoding circuit is performed without exchanging additional classical information for parameter estimation. In case ③, the base station performs parameter estimation and n=28, k=2, d=10, t corr =1 value is transmitted to the terminal, and then quantum error correction code decoding and subsequent processes are performed.

[0550] In case ①, the base station and terminal require additional memory to store the table, but adaptive mode EDP can be performed without additional communication for optimization function and parameter exchange.

[0551] In case ②, the base station and terminal do not require additional memory for table storage, but complexity is added for optimization in the base station or terminal.

[0552] In case ③, the base station and terminal do not require additional memory for table storage, but the base station adds complexity for optimization and classical communication for parameter exchange.

[0553] In the above scenarios, it is assumed that the initial fidelity has been measured by the base station and the terminal in advance, using a predefined method, prior to the entanglement distillation process. In this case, ① and ② require that both the base station and the terminal share the same initial fidelity measurements through the initial fidelity measurement process. In ③, only the base station estimating the parameters needs to know the initial fidelity. Therefore, ① and ② involve sharing the initial fidelity measurements during the initial fidelity measurement process.

[0554] In the above invention, if both the base station and the terminal have a look-up table, but the information on the initial fidelity is only available at the base station or the terminal, the base station or terminal with the initial fidelity can derive only the index corresponding to the optimal EDP protocol and specification from the look-up table and transmit the index to the terminal or base station without the initial fidelity to operate the optimal protocol. For example, if only the base station knows the initial fidelity and the index of QPA is 0 and the index of QECCs is 1, F = 0.9, F target =1-10 -6 If , 0 is transmitted, and F=0.96,F target =1-10-6 When 1 and after n,k,d,t corr Transmits values ​​to the terminal.

[0555]

[0556] If constraints other than initial and target fidelity exist, they are added to the constraints section of the optimization problem. For example, if there is a limit (n≤N) on the number of EPR states that can be transmitted and received at once, optimization is performed as shown in Equation 26 below.

[0557]

[0558] For the above-mentioned Tables 6 to 8, the parameter values ​​are optimized through the n≤31 setting. The Look-up Table corresponding to the optimal EDP protocol and spec shown in the example is the initial fidelity (F), the target fidelity F target In addition, it can be individually configured according to the number (N) of EPR states that can be transmitted and received, and it is assumed that the base station and the terminal both have individual look-up tables in a pre-agreed manner.

[0559]

[0560] EDP ​​and fidelity-based parameter estimation process using adaptive mode

[0561] Figure 41 is a diagram illustrating an example of the overall block diagram of the adaptive mode EDP technique in a system applicable to the present disclosure. Specifically, Figure 41 is an overall block diagram integrating the two proposed techniques described above.

[0562] In an environment where initial and target fidelities are given as constraints, the final resource consumption is estimated to determine whether entanglement distillation will be performed via QPA or 2-way QECCs for the base station and terminal, respectively. Additional constraints, such as the number of EPR states that can be transmitted and received simultaneously, may exist.

[0563] When utilizing 2-way QECCs EDP, the base station and terminal share QECCs parameters and t_corr. When utilizing QPA, the base station and terminal share the number of QPA rounds.

[0564] When utilizing QPA, the base station and terminal operate in the same manner as the bidirectional EDP protocol described above. When utilizing 2-way QECCs, the base station and terminal perform the process of the QECCs-based bidirectional EDP technique described above.

[0565] Referring to Figure 41, in performing EDP in adaptive mode,

[0566] (1) Select one of the EDPs, either QPA or 2-way QECCs.

[0567]

[0568] (1-1) If QPA is selected as EDP:

[0569] (2) Generate EPR pairs.

[0570] (3) Transmit particles of EPR pairs.

[0571] (4) Perform QPA unitary operations.

[0572] (5) Measure the operation results.

[0573] (6) Share the measurement results. Determine whether the shared and received measurement results are identical. If they are not identical, discard the EPR state. If they are identical, terminate the process.

[0574]

[0575] (1-2) If 2-way QECCs are selected as EDP:

[0576] (2) Generate EPR pairs.

[0577] (3) Transmit particles of EPR pairs.

[0578] (4) Perform QECCs decoding circuit operations.

[0579] (5) Measure the operation results.

[0580] (6) Share the measurement results.

[0581] (7) Multiply the shared measurement results with the shared measurement results. Determine whether the result of the multiplication is included in a predetermined set, i.e., related to the established pattern. If the result of the multiplication is not included in the predetermined set, the EPR state is discarded.

[0582] (8) If the result of the multiplication is included in a predetermined set, the EPR state is modified.

[0583]

[0584] Minimum Fidelity Sharing in EDP used in Adaptive mode

[0585] When executing a single round of the EDP protocol in QPA, two low-fidelity EPR pairs are used to generate one high-fidelity EPR pair. To determine whether a high-fidelity EPR pair has been generated, the sender and receiver each measure one of their EPR pairs and share the measurement results. In other words, in Figure 3, the sender and receiver each measure the second qubit and share the measurement result (s1, s2∈{0,1}). (-1) s1+s2 If = 1, one unmeasured EPR pair is preserved, otherwise one unmeasured EPR pair is discarded. At this time, as shown in Tables 6, 7, and 8, various numbers of rounds are performed depending on the input fidelity and target fidelity. The EPR pairs generated after the corresponding rounds always satisfy the target fidelity. Table 9 below shows the product ((-1)) of the measured results according to various input fidelities. s1+s2 ) is the fidelity for the actual protocol. In the actual protocol, it is (-1) s1+s2 Only the EPR state for cases where =1 is preserved and utilized. Table 9 shows the fidelity according to the measurement results in the QPA protocol.

[0586] Input Fidelity (F init )(-1) s1+s2 If =1, then fidelity(-1) s1+s2 =1 if fidelity0.60.6204380.250.70.7352940.250.80.838150.250.90.9263960.25

[0587] In contrast, when performing a two-way protocol based on QECCs, syndrome ((-1) s1+s2 ) this t corr Even if it is included in the set of syndromes that are assumed to be errors below, the fidelity of the preserved EPR pair varies depending on the pattern of the syndrome. For example, the quantum error correction code [[5,1,3]] is corr When =1 is used, EPR pairs are preserved when a total of 16 syndrome patterns are generated. The fidelity of the EPR pairs preserved according to each syndrome pattern is as follows. Table 10 shows [[5,1,3]], t corr When using a quantum error-correcting code with =1, it represents the fidelity of the EPR pair according to the syndrome pattern. In this case, S in Table 10 is defined as follows.

[0588]

[0589]

[0590] Fidelity by Syndrome PatternAverage FidelityInput Fidelity(F init )0000s∈S0.60.7535730.3751820.4156560.70.919130.4882350.567520.80.9828770.6346820.750850.90.9984770.8076140.920492

[0591] For example, in an environment with input fidelity 0.9, the quantum error correction code [[5,1,3]] is t corr=1 (i.e., used as QECCs), an output fidelity of 0.92 is obtained on average. However, depending on the syndrome pattern being measured, if 0000 is measured as the syndrome output value, an EPR pair with a fidelity of 0.998 is preserved, but in the case of other syndromes (e.g., 0001), an EPR pair with a fidelity of 0.808 is obtained after error correction, thereby preserving an EPR pair with a lower output fidelity compared to the input fidelity.

[0592] [[7,1,3]] quantum error correction code t corr When =1 is used, EPR pairs are preserved when a total of 22 specific syndrome patterns are generated. The fidelity of the EPR pairs preserved according to each syndrome pattern is as follows. Table 11 shows [[7,1,3]], t corr When using a quantum error-correcting code with =1, it represents the fidelity of the EPR pair according to the syndrome pattern. In this case, S in Table 11 is defined as follows.

[0593]

[0594] Fidelity by Syndrome PatternAverage FidelityInput Fidelity(F init )000000s∈S0.60.778840.4574520.4889940.650.875750.5342830.5794240.70.9361930.6137720.6716020.750.9700750.6913150.7586220.80.9875110.7640980.8358730.850.9956570.8310230.9007890.90.9989260.892160.9517210.980.9998860.948190.986428

[0595] For example, in an environment with input fidelity 0.8, the quantum error correction code [[7,1,3]] is t corr=1 (i.e., used as QECCs), an output fidelity of 0.836 is obtained on average. However, depending on the syndrome pattern being measured, when 000000 is measured as the syndrome output value, an EPR pair with a fidelity of 0.988 is preserved, but in the case of other syndromes (e.g., 000001), an EPR pair with a fidelity of 0.764 is obtained after error correction, thereby preserving an EPR pair with a lower output fidelity compared to the input fidelity.

[0596]

[0597] FIG. 42 is a diagram illustrating an example of the average and minimum fidelity performance of a two-way EDP in a system applicable to the present disclosure.

[0598] Specifically, Fig. 42 shows the average and minimum fidelity performance of two-way EDP using [[5,1,3]] QECCs and [[7,1,3]] QECCs in adaptive mode.

[0599] In Fig. 42, F avg ([[5,1,3]]) represents the average fidelity performance of two-way EDP using [[5,1,3]].

[0600] F min ([[5,1,3]]) represents the minimum fidelity performance of two-way EDP using [[5,1,3]]. F min ([[5,1,3]]) is a quantum error correcting code of [[5,1,3]] corr This is when =1 is used.

[0601] F avg ([[7,1,3]]) represents the average fidelity performance of two-way EDP using [[7,1,3]].

[0602] F min ([[7,1,3]]) represents the minimum fidelity performance of two-way EDP using [[7,1,3]]. F min([[7,1,3]]) is a quantum error correcting code of [[7,1,3]] corr This is when =1 is used.

[0603]

[0604] Minimum Fidelity Share Protocol

[0605] FIG. 43 is a diagram illustrating an example of the entire process of an Adaptive mode-based EDP technique considering minimum fidelity in a system applicable to the present disclosure.

[0606] A specific syndrome (e.g., s∈S) generates EPR pairs with lower fidelity than the average fidelity of the entire syndrome pattern. Therefore, this disclosure proposes a method for additionally sharing fidelity based on specific syndromes in an adaptive mode. Based on the shared minimum fidelity, the sender and receiver then discard or utilize the shared EPR state, as needed, to perform EDP.

[0607] The execution process of the proposed technique is as follows.

[0608] The process of the adaptive mode-based EDP technique is as follows. First, when the QPA mode is selected as the adaptive mode, the bidirectional EDP protocol of Fig. 43 is performed. The following describes the process of the QECCs-based bidirectional EDP technique as the adaptive mode, and the process of dividing the QPA mode and the QECCs-based bidirectional EDP mode is described in [Fidelity-based QECCs parameter estimation process].

[0609] ① When utilizing the [[n,k,d]] quantum error correction code, the sender (Alice) generates n EPR states using nonlinear elements.

[0610] ② The sender transmits one qubit from each EPR state to the receiver (Bob). At this time, the quantum channel includes the generation defect of the EPR state, a wired / wireless photonic channel, and a quantum memory error channel.

[0611] ③ The sender and receiver perform the decoding related circuit of the [[n,k,d]] quantum error correction code promised for the Unitary Operation operation as a unitary operation.

[0612] ④ The sender and receiver each measure (nk) qubits. At this time, the location of the measured qubit is determined based on the characteristics of the [[n,k,d]] quantum error correction code.

[0613] ⑤ The receiver transmits the measurement result of (nk) bits to the sender through the Classical Channel.

[0614] ⑥ The sender multiplies its own measurement result with the measurement result of the recipient, and according to the pattern of the multiplied value, performs a correction operation on the k EPR states that were not measured in step ④, and then preserves or discards the EPR states. If the EPR states are discarded, steps ① to ⑤ are performed again. When s1(∈{-1,1}) is Alice's measurement result and s2(∈{-1,1}) is Bob's measurement result, the product of the measurement results ((-1) s' =s1s2) belongs to the promised pattern S, the sender performs a correction operation on his qubit among the unmeasured EPR states (k). The correction operation is determined as one of I, X, Y, or Z according to the pattern of s', and operates on the sender's qubit among the EPR pair states of the unmeasured EPR states.

[0615] ⑦ If the EPR state is preserved during the execution of ⑥, the sender estimates the fidelity of k EPR states based on the product of the measurement results and shares this estimated fidelity with the receiver by transmitting it over a classical channel. The method for estimating the minimum fidelity is described below. At the same time, the sender informs the receiver that the EPR state is preserved.

[0616] In this disclosure, the method of preparing an EPR pair at the sender (Alice) and transmitting one qubit from each EPR pair from the sender to the receiver (Bob) is described uniformly for convenience of explanation. However, it is self-evident that the method of preparing an EPR pair at a third-party node and transmitting it to the first node (Alice) and the second node (Bob) to perform all the methods described above, or that it can be applied equally even if the sender and receiver are switched. ⑤ If Alice and Bob share the measurement results with each other, they can estimate the minimum fidelity.

[0617] It is also self-evident that Alice and Bob can transmit the measurement results to each other, calculate the syndromes respectively, and then Bob (or Alice) can perform a correction operation to estimate the minimum estimate value for Alice (or Bob).

[0618] ⑦ In the process, after the transmitter and receiver perform the adaptive mode, they perform additional communication as shown in Fig. 43 to share the minimum fidelity. Afterwards, they decide whether to discard or preserve the EPR state preserved in the previous step as needed. Although it varies depending on the case, as an example of preserving the EPR state, if the fidelity of the entanglement state previously shared through the EDP protocol is high, even an entangled pair that does not satisfy the target fidelity can be preserved. For example, when an EPR entanglement state is needed after a total of 10 distillation protocols, when sharing the 10th entanglement state, if the fidelity is sufficiently high considering the fidelities of the previous 9, it can be preserved even if the fidelity of the last EPR entanglement state does not reach the target fidelity.

[0619]

[0620] [[n,k,d]],t corr Estimation technique with minimum fidelity when using QECCs in two ways

[0621] When utilizing two-way QECCs in adaptive mode, [[n,k,d]],t satisfy the target fidelity on average. corr A parameter estimation process exists. However, depending on the syndrome measurement results, there are cases where the target fidelity is not met. Therefore, an additional process is needed to report the fidelity for each syndrome after its occurrence. The present disclosure provides a method for estimating fidelity when a specific syndrome occurs.

[0622] F=F init If a syndrome occurs for an error in the weight w in the initial fidelity of the output state, the minimum fidelity (F') of the output state min ) has a lower bound as in mathematical expression 27, and in this disclosure, the output fidelity is estimated through the bound.

[0623]

[0624] The probability of performing a correction operation is p succ is defined as the probability of success (p succ ) is estimated as in mathematical expression 28, in the same way as when the existing QECCs are used in adaptive mode.

[0625]

[0626] FIG. 44 is a diagram illustrating an example of minimum fidelity estimation performance when using a two-way [[7,1,3]] error correction code in a system applicable to the present disclosure.

[0627] FIG. 45 is a diagram illustrating an example of minimum fidelity estimation performance when a two-way [[5,1,3]] error correction code is used in a system applicable to the present disclosure.

[0628]

[0629] Average fidelity (F') performance and minimum fidelity (F') performance when using [[7,1,3]],[[5,1,3]] QECCs in adaptive mode with t_corr=1min ) Performance and estimation (Est) are as shown in Fig. 44 and Fig. 45. Fig. 44 and Fig. 45 show the estimated performance when using [[7,1,3]] and [[5,1,3]] QECCs, respectively. In Fig. 44 and Fig. 45, the solid line represents the estimated value of fidelity, and the dotted line represents the actual value of fidelity. In Fig. 44 and Fig. 45, est F min ,t corr For the line corresponding to =0, the EPR state is preserved after correction for syndrome patterns estimated to have 0 or 1 error, and in other cases, the EPR state is discarded. In Figs. 44 and 45, est F min ,t corr For the line corresponding to =1, the EPR state is preserved only for syndrome patterns in which no error occurs, and the EPR state is discarded in other cases.

[0630] Figures 44 and 45 show that the fidelity of the preserved EPR state is greater than the estimated fidelity, as the estimate of the minimum fidelity is lower than the actual fidelity.

[0631]

[0632] Minimum fidelity based n,k,d,t corr Parameter estimation process

[0633] When using QECCs in adaptive mode, the minimum fidelity of the EPR state generated after the EDP process is the target fidelity (F target ) to utilize the minimum EPR state (fidelity F) while satisfying the parameters n, k, d of QECCs and parameter t of adaptive mode. corr Or or t corr To estimate , the optimization process of the following mathematical expression 29 is performed.

[0634]

[0635] At this time, the minimum estimated fidelity F'min can be estimated through mathematical expression 27.

[0636]

[0637] F1-F target modeNum_EPRnkdt corr GainQPA Round0.851.00E-06'QPA'792.9544'-''-''-''-''-'60.861.00E-06'QPA'654.7041'-''-''- ''-''-'60.871.00E-06'QPA'542.753'-''-''-''-''-'60.881.00E-06'QPA'156.4529'-''-' '-''-''-'50.891.00E-06'QPA'135.3346'-''-''-''-''-'50.91.00E-06'QPA'117.3719'-'' -''-''-''-'50.911.00E-06'QPA'102.049'-''-''-''-''-'50.921.00E-06'QPA'88.9412'-'' -''-''-''-'50.931.00E-06'QPA'77.6981'-''-''-''-''-'50.941.00E-06Detecting'47.83 9927390-20.1893'-'0.951.00E-06Detecting'35.950527390-23.7426'-'0.961.00E-06Dete cting'25.049626380-27.4389'-'0.971.00E-06Detecting'10.707225570-10.585'-'0.981. 00E-06Detecting'8.285525570-11.047'-'0.991.00E-06Detecting'4.697816450-3.8568'-'

[0638]

[0639] F1-F target modeNum_EPRnkdt corrGainQPA Round0.851.00E-07'QPA'792.9544'-''-''-''-''-'60.861.00E-07'QPA'654.7041'-''-''-''-''-'60.871.00E-07'QPA'542.753'-''-''-''-''-'60.881.00E-07'QPA'451.691'-''-''-''-''-'60.891.00E-07'QPA'377.3025'-''-''-''-''-'60.91.00E-07'QPA'316.2845'-''-''-''-''-'60.911.00E-07'QPA'102.049'-''-''-''-''-'50.921.00E-07'QPA'88.9412'-''-''-''-''-'50.931.00E-07'QPA'77.6981'-''-''-''-''-'50.941.00E-07'QPA'68.0292'-''-''-''-''-'50.951.00E-07Detecting'53.925827290-5.7673'-'0.961.00E-07Detecting'27.096927390-25.3916'-'0.971.00E-07Detecting'19.133226380-27.114'-'0.981.00E-07Detecting'8.285525570-32.5425'-'0.991.00E-07Detecting'5.186921560-12.3894'-'

[0640]

[0641] F1-F target modeNum_EPRnkdt corrGainQPA Round0.9991.00E-06Detecting'1.921317940-2.0947'-'0.99911.00E-06Detecting'1.91817940-2.0964'-'0.99921.00E-06Detec ting'1.914817940-2.098'-'0.99931.00E-06Detecting'1.911517940-2.0997'-'0.99941.00E-06Detecting'1.908317940-2.1013' -'0.99951.00E-06Detecting'1.90517940-2.103'-'0.99961.00E-06Detecting'1.901817940-2.1046'-'0.99971.00E-06Detectin g'1.892301650-2.1128'-'0.99981.00E-06Detecting'1.8349311750-2.1683'-'0.99991.00E-06Detecting'1.50096420-2.5007'-'

[0642]

[0643] Table 12 is F target =1-10 -6 It represents the minimum fidelity based EDP protocol and spec (0.85≤F≤0.99).

[0644] Table 13 is F target =1-10 -7 It represents the minimum fidelity based EDP protocol and spec (0.85≤F≤0.99).

[0645] Table 14 is F target =1-10 -6 It represents the minimum fidelity based EDP protocol and spec (0.999≤F≤0.9999).

[0646] Table 12, Table 13, Table 14 show the specific goal fidelity (F target =1-10 -6 or 1-10 -7) is the result of the optimal mode and parameter estimation to satisfy the condition. The mode is determined among the adaptive modes QPA, QECCs (QECCs-based bidirectional EDP technique), and Detecting, and means the EDP operation method that utilizes the minimum resources in each initial fidelity. 'QPA' is QPA mode, and 'QECCs' is QECCs-based bidirectional EDP protocol. corr If ≥1, 'Detecting' is t in a bidirectional EDP protocol based on QECCs. corr =0. Num_EPR is the number of EPR states utilized in each mode. n,k,d,t corr When a bidirectional protocol based on QECCs is utilized in mode, the optimal code and number of corrected errors (t) corr ) and is an estimated value using bound. Gain is the number of qubits reduced when a bidirectional protocol based on QECCs is used compared to QPA, and QPA round is the number of rounds when used in QPA mode.

[0647] For example, in an environment with an initial fidelity of 0.95, 1-10 -7 If you want to satisfy the fidelity of , the quantum error correction code [[29,1,11]] is used as the standard for the average fidelity (Table 7). corr =1 can be used, but based on the minimum fidelity (Table 13), the quantum error correction code [[27,2,9]] is t corr =0 should be used. The method based on average fidelity compared to QPA uses 8.8 fewer EPR pairs, while the method based on minimum fidelity uses 5.7 fewer EPR pairs.

[0648]

[0649] FIG. 46 is a diagram illustrating an example of the entire process of a minimum fidelity-based parameter estimation and an adaptive mode-based EDP technique in a system applicable to the present disclosure.

[0650] According to Fig. 46, a method is shown for estimating the parameters of the Adaptive mode based on the minimum fidelity criterion and performing a correction operation based on the parameters or discarding the EPR state and performing the protocol again.

[0651]

[0652] Entangled State Reuse in EDP Using Two-way QECCs

[0653] As known from the patent that performs EDP in adaptive mode based on minimum fidelity, the fidelity value of an unmeasured entangled pair varies depending on the syndrome of QECCs. For example, the fidelity by measurement syndrome when using the [[5,1,3]] quantum error correction code is as shown in Table 15 below. Table 15 shows the fidelity of an EPR pair according to the syndrome pattern when using the [[5,1,3]] quantum error correction code.

[0654]

[0655] Fidelity by Syndrome PatternAverage FidelityInput Fidelity(F init )0000s∈S0.60.7535730.3751820.4156560.70.919130.4882350.567520.80.9828770.6346820.750850.90.9984770.8076140.920492

[0656] For example, in an environment with input fidelity 0.9, the quantum error correction code [[5,1,3]] is t corr=1 (i.e., used as QECCs), an output fidelity of 0.92 is obtained on average. However, depending on the syndrome pattern being measured, if 0000 is measured as the syndrome output value, an EPR pair with a fidelity of 0.998 is preserved, but in the case of other syndromes (e.g., 0001), an EPR pair with a fidelity of 0.808 is obtained after error correction, thereby preserving an EPR pair with a lower output fidelity compared to the input fidelity.

[0657] For example, if only the [[5,1,3]] error correction code is used, the initial fidelity is 0.9, and the target fidelity is 0.91, and if the target fidelity is set based on the average fidelity, the [[5,1,3]] code will correct and preserve unmeasured qubits for all syndromes (t corr =1), if the target fidelity is set based on the minimum fidelity, the qubit is preserved only when the 1010 syndrome is measured (t corr (This is equivalent to performing two-way QECCs EDP for the case where =0). If we think of this process as utilizing the [[5,1,3]] quantum error correction code four times for 20 entangled pairs (all four times measuring syndromes except 0000), then based on the minimum fidelity, we discard four unmeasured entangled pairs.

[0658] In this disclosure, we propose a technique to additionally utilize unmeasured EPR pairs instead of discarding them when discarded syndromes are measured. In the example above, when performing two rounds of QPA using four EPR pairs, one EPR pair with a fidelity of 0.949 can be generated according to the fidelity formula after performing QPA in Equation 22, and an entangled state exceeding the target fidelity of 0.91 is generated. In addition to QPA, various types of EDP protocols can be performed (e.g., two-way QECCs, QECCs, ...) and can be operated in various operating modes (e.g., recurrence method, entanglement pumping mode, ...). In addition, the criteria for reusing entangled pairs that should be discarded can also be set in various ways (e.g., F'≥F init , F'>0.5 (whether QPA can be used)).

[0659] For two-way QECCs EDP that reuses entanglement state, parameters n, k, d, t corr In addition t reuse A parameter is introduced, t reuse is the maximum weight of the estimation error that can reuse the entanglement state. In the example above, to meet the target fidelity of 0.91 as the minimum fidelity criterion, t corr =0 is used (t in case of using only 0000 syndrome) corr ), additionally, if the QPA protocol is utilized for entangled pair reuse, all syndromes can be utilized, so t reuse =1.

[0660]

[0661] Two-way QECCs EDP protocol that recycles entangled states

[0662] FIG. 47 is a diagram illustrating an example of the entire process of a Two-way QECCs EDP protocol for reusing entangled qubits in a system applicable to the present disclosure.

[0663] As described above, various criteria can be used to select the EPR reuse criterion (select t_reuse), and the formula utilized during the optimization process can also be modified. In this regard, the protocol execution process is described when the existing optimization is performed in the same manner as the existing adaptive mode (i.e., the optimization formula remains the same as before), and entangled pairs are reused based on measurement results.

[0664] The process of the adaptive mode-based EDP technique is as follows. First, when the QPA mode is selected as the adaptive mode, the bidirectional EDP protocol of Fig. 28 is performed. The following description is the process of reusing the EPR state in the process of the QECCs-based bidirectional EDP technique as the adaptive mode. The process of dividing the QPA mode and the QECCs-based bidirectional EDP mode is described as [average fidelity-based parameter estimation process] or [minimum fidelity-based n,k,d,t corr It is the same as the parameter estimation process. Additionally, t reuse To select parameters, the entanglement state reuse criterion is selected. Figure 47 shows that when the [[n,k,d]] symbol is used, the output fidelity F' is greater than the input fidelity F init Bigger than t reuse Here is an example of when is set.

[0665] ① When using the [[n,k,d]] quantum error correction code, the sender (Alice) generates n EPR states using nonlinear elements.

[0666] ② The sender transmits one qubit from each EPR state to the receiver (Bob). At this time, the quantum channel includes the generation defect of the EPR state, a wired / wireless photonic channel, and a quantum memory error channel.

[0667] ③ The sender and receiver perform the decoding circuit of the [[n,k,d]] quantum error correction code promised for the Unitary Operation operation as a unitary operation.

[0668] ④ The sender and receiver each measure (nk) qubits. At this time, the location of the measured qubit is determined based on the characteristics of the [[n,k,d]] quantum error correction code.

[0669] ⑤ The receiver transmits the measurement result of (nk) bits to the sender through the Classical Channel.

[0670] ⑥ The sender multiplies its own measurement result with the measurement result of the recipient, and performs a correction operation on the k EPR states that were not measured in step ④ according to the pattern of the product value. Then, the sender decides whether to (a) preserve them, (b) perform an additional EDP on the unmeasured EPR states, or (c) discard the EPR states. If the EPR states are discarded (case (c)), steps ① to ⑤ are performed again. When s1(∈{-1,1}) is Alice's measurement result and s2(∈{-1,1}) is Bob's measurement result, if the product of the measurement results (s'=s1s2) belongs to the agreed pattern S, the sender performs a correction operation on its own qubit among the k unmeasured EPR states (case (a)). The correction operation is determined as one of I, X, Y, and Z according to the pattern of s', and operates on the sender's qubit among the EPR pair states of the unmeasured EPR states. Although s' does not belong to S, it additionally determines whether to perform EDP based on various criteria (case (c)). Figure 50 is an example of determining whether to perform EDP by comparing the fidelity of an unmeasured qubit with the initial fidelity. In this case, the fidelity of an unmeasured qubit is [[[n,k,d]],t corr When using QECCs in two ways, the weight of the error associated with the syndrome measured in the [Minimum Fidelity Estimation Technique] can be estimated as w.

[0671] ⑦ If the EPR state is preserved during the execution of ⑥, the sender estimates the fidelity of k EPR states based on the product of the measurement results and shares the estimated fidelity with the receiver by transmitting it through a classical channel (minimum fidelity sharing is optional. It is unnecessary when based on average fidelity). At the same time, the sender notifies the receiver that the EPR state is preserved.

[0672] In the case of reusing the EPR state during the execution process of ⑥, EDP is performed with an unmeasured qubit (N, F'≥F in s∈S based on the drawing init (if Y in ).

[0673] For convenience of explanation, the methods for preparing EPR pairs at the sender (Alice) and transmitting one qubit from each EPR pair to the receiver (Bob) described in this disclosure are described uniformly. However, it is self-evident that the method of preparing EPR pairs at a third-party node and transmitting them to the first node (Alice) and the second node (Bob) to perform all the methods described above, or even if the sender and receiver are changed, can be applied equally.

[0674] In addition, it is self-evident that in process ⑤, Alice and Bob transmit the measurement results to each other, and after each calculates the syndrome, Bob (or Alice) performs the correction operation and Alice (or Bob) can estimate the fidelity estimate.

[0675] Also t reuse It is obvious that Alice and Bob can initially agree on parameters and perform the protocol without performing it, and then calculate the fidelity based on the measured syndrome to check whether the initial entanglement state reuse criterion is satisfied and decide whether to perform EDP.

[0676]

[0677] (Example) [[n,k,d]] and t based on Average Fidelity corr After optimizing the parameters, a method for reusing entangled states using QPA

[0678] Case 1) F'≥F init satisfies t reuse If you set

[0679] Target fidelity based on average fidelity F_target=1-10 -6 Initial fidelity F inputWhen performing the parameter optimization in adaptive mode for =0.85~0.99, the results are as shown in Table 16. In the technique utilizing qubit reuse, 't' is shown in Table 17. reuse ' parameter is additionally utilized, and its value is the maximum weight of the estimation error that can reuse the entangled state. Table 17 shows that the fidelity of the reused entangled state is the initial fidelity (F input ) is used in cases where the fidelity of reused is higher than t reuse It is the fidelity of the unmeasured entangled state when estimated by the error of the weight corresponding to , and it can be confirmed that it is higher than the initial fidelity. reuse If the error is estimated to be greater than 1, the fidelity is 'fidelity of {t reuse It is marked as '+1}' and has a lower value than the initial fidelity. 'prob of {t corr}' is t corr The lower bound of the probability of occurrence of the following number of errors is 'prob of {t reuse}' is t reuse The lower bound of the probability of the following number of errors occurring is approximately the prob of {t reuse}- prob of {t corr} It can be estimated that k qubits can be recycled with a probability corresponding to the number of qubits.

[0680] For example, if the initial fidelity is 0.98 (F_input=0.98), 1-10 -6 Fidelity of the goal (F target =1-10 -6 ) to satisfy n=25,k=5,d=7,t corrBy utilizing the parameter of =0, we could not generate k=5 entangled pairs for syndromes where the weight of the error is estimated to be 1. However, through qubit recycling, we confirmed that the initial fidelity is higher than 0.98 for syndromes where the weight of the error is estimated to be 1, so we can perform EDP later by transmitting only the corresponding qubit or additional entangled states. If the QPA protocol is performed with only the corresponding 5 entangled states, two rounds of QPA can be performed, and then 1-F'=8.0056×10 -08 Fidelity of (F'≥F target ) can be created.

[0681] For an initial fidelity of 0.95, k=2 entangled states are created, and t reuse For the syndrome corresponding to =2, EDP can be performed by confirming that the initial fidelity is higher than 0.95. However, even if QPA of 1 round is performed immediately, the target fidelity cannot be exceeded, so QPA cannot be used. In this case, the two entangled states are preserved, and only nk qubits are transmitted in the subsequent entangled pair transmission for EDP, and [[n,k,d]] QECCs are then performed at t corr It performs EDP based on two-way QECCs.

[0682] Table 16 shows the F when reusing EPR states with fidelity greater than the initial fidelity. target =1-10 -6 Indicates an average fidelity-based EDP protocol.

[0683] Table 17 shows the F when reusing EPR states with fidelity greater than the initial fidelity. target =1-10 -6 It represents the qubit recycling specification in .

[0684]

[0685] F input 1-F target modeNum_EPRnkdtcorr GainQPA Round0.851.00E-06'QPA'792.9544'-''-''-''-''-'60.861.00E-06'QPA'654.7041'-''-''-''-''-'60.871.00E-06'QPA'542.753'-''-''-''-''-'60.881.00E-06'QPA'156.4529'-''-''-''-''-'50.891.00E-06'QPA'135.3346'-''-''-''-''-'50.91.00E-06'QPA'117.3719'-''-''-''-''-'50.911.00E-06'QPA'102.049'-''-''-''-''-'50.921.00E-06'QPA'88.9412'-''-''-''-''-'50.931.00E-06'adaptive'74.7453291111-2.952850.941.00E-06'Detecting'47.839927390-20.1893'-'0.951.00E-06'adaptive'23.797282101-35.8961'-'0.961.00E-06'adaptive'20.2648282101-32.2237'-'0.971.00E-06'adaptive'11.162427391-10.1298'-'0.981.00E-06'Detecting'8.285525570-11.047'-'0.991.00E-06'Detecting'4.697816450-3.8568'-'

[0686] t reuse fidelity_of_reusedfidelity_of_(t reuse +1)prob_of_{t corr }prob_of_{t reuse}'-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-'20.994990.111030.387980.6683710.997920 .17830.188130.5123520.971490.0103780.588310.8373420.992830.0260030.690850.900110.999990.890410.806280.8062810.999690.128480.603460.91135010.961660.851460.85146

[0687]

[0688] Case 2) t satisfies F'>0.5 reuse If you set

[0689] F is the target fidelity based on the average fidelity target =1-10 -6 Initial fidelity F input When performing parameter optimization in adaptive mode for =0.85~0.99, the results are as shown in Table 18. In the technique utilizing qubit reuse, 't' is shown in Table 19. reuse ' parameter is additionally utilized, and its value is the maximum weight of the estimation error that can reuse the entangled state. In the figure below, it is utilized when the fidelity of the reused entangled state is higher than 0.5, which is the minimum fidelity for QPA to operate. 'fidelity of reused' is t reuse It is the fidelity of the unmeasured entanglement state when estimated by the error of the corresponding weight, and it can be confirmed that it is higher than 0.5. reuse If the error is estimated to be greater than 1, the fidelity is 'fidelity of {t reuse It is marked as '+1}' and has a lower value than the initial fidelity. 'prob_of_{t corr}' is t corr The lower bound of the probability of occurrence of the following number of errors is 'prob_of_{t reuse}' is t reuse The lower bound of the probability of occurrence of the following number of errors is approximately prob_of_{t reuse}- prob_of_{t corr} It can be estimated that k qubits can be recycled with a probability corresponding to the number of qubits.

[0690] Table 18 shows the F when reusing EPR states with fidelity greater than 0.5. target =1-10 -6 Indicates an average fidelity-based EDP protocol.

[0691] Table 19 shows the F when reusing EPR states with fidelity greater than 0.5. target =1-10 -6 It represents the qubit recycling specification in .

[0692]

[0693] F input 1-F target modeNum_EPRnkdt corrGainQPA Round0.851.00E-06'QPA'792.9544'-''-''-''-''-'60.861.00E-06'QPA'654.7041'-''-''-''-''-'60.871.00E-06'QPA'542.753'-''-''-''-''-'60.881.00E-06'QPA'156.4529'-''-''-''-''-'50.891.00E-06'QPA'135.3346'-''-''-''-''-'50.91.00E-06'QPA'117.3719'-''-''-''-''-'50.911.00E-06'QPA'102.049'-''-''-''-''-'50.921.00E-06'QPA'88.9412'-''-''-''-''-'50.931.00E-06'adaptive'74.7453291111-2.952850.941.00E-06'Detecting'47.839927390-20.1893'-'0.951.00E-06'adaptive'23.797282101-35.8961'-'0.961.00E-06'adaptive'20.2648282101-32.2237'-'0.971.00E-06'adaptive'11.162427391-10.1298'-'0.981.00E-06'Detecting'8.285525570-11.047'-'0.991.00E-06'Detecting'4.697816450-3.8568'-'

[0694] t reuse fidelity_of_reusedfidelity_of_(t reuse +1)prob_of_{t corr }Prob_of_{t reuse}'-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-''-'20.994990.111030.387980.6683710.997920.1783 0.188130.5123520.971490.0103780.588310.8373420.992830.0260030.690850.900120.890410.000862740.806280.953810.999690.128480.603460.9113510.961660.000284270.851460.98907

[0695]

[0696] For example, previously the initial fidelity was 0.98 (F input If =0.98), 1-10 -6 Fidelity of the goal (F target =1-10 -6 ) to satisfy n=25,k=5,d=7,t corr By utilizing the parameter of =0, we could not generate k=5 entangled pairs for syndromes where the weight of the error is estimated to be 1. However, through qubit recycling, we confirmed that the fidelity is higher than 0.5 for syndromes where the weight of the error is estimated to be 1, so we can perform EDP later by transmitting only the corresponding qubit or an additional entangled state. If the QPA protocol is performed with only the corresponding 5 entangled states, 2 rounds of QPA can be performed, and then 1-F'=8.0056×10- 08 Fidelity of (F'≥F target ) can be created.

[0697] Since k entangled states with lower output fidelity than Case 1 can be utilized, t in Case 1 reuse t in case 2 is higher than the value reuseThe value is greater than or equal to . For example, if the initial fidelity is 0.97, t in Table 16 and Table 17 (case 1) reuse The value is 1, whereas t in Table 18 and Table 19 (case 2) reuse The value increases by 2, so the probability of not being discarded is {t reuse}) also increases from 0.81 to 0.95.

[0698]

[0699] (Example) [[n,k,d]] and t based on Average Fidelity corr After optimizing the parameters, a method for reusing the entangled state using adaptive mode

[0700] (8) When the Adaptive mode EDP technique is used in the EDP technique, an additional entanglement distillation process is performed using k recycled entangled qubits. At this time, the parameters of the Adaptive mode EDP (n', k', d', t corr ') An optimization process can be utilized for optimization, and the optimization process is as shown in the mathematical expression 30 below.

[0701]

[0702] Here, F" is the fidelity of the k' entangled states preserved after (8) distillation.

[0703] F'≥F init Recycling parameter t reuse When using (Table 16 and Table 17), F init If 5 qubits are recycled at =0.98, [[4,2,2]] QECCs are t corr After performing additional EDP using '=0, 1-F"=6.4106×10 -07 You can obtain two entangled states.

[0704]

[0705] (Example) [[n,k,d]] and t based on Average Fidelity corr Parameter t reuse When optimizing with parameters

[0706] n,k,d,t corr is selected and t reuse Rather than determining n,k,d,t corr ,t reuse When selecting parameters by comprehensively considering , the optimization process of mathematical expression 31 or mathematical expression 32 below can be performed (when QPA is used as (8) EDP)

[0707]

[0708] or

[0709]

[0710] It is self-evident that optimization functions and constraints can be changed or added depending on the usage environment.

[0711]

[0712] Additional resource reduction techniques in Two-way QECCs EDP

[0713] Figure 48 shows the success probability (p) according to the α value when using the [[5,1,3]] quantum error correction code in a system applicable to the present disclosure. succ ) is a drawing showing an example.

[0714] FIG. 49 is a diagram illustrating an example of the average fidelity (F') of an EPR pair according to the value of α when utilizing a [[5,1,3]] quantum error correction code in a system applicable to the present disclosure.

[0715] As described above, in the present disclosure (n, k, d, t corr ) in two-way QECCs EDP corr We propose a technique to correct some errors with weight +1. Through this, the fidelity of the output entangled state is (n, k, d, t corr) is lower than the EDP of two-way QECCs, but the success probability increases. (n, k, d, t) corr )=(5,1,3,0) in two-way QECCs EDP corr The fidelity and success probability of the entanglement state when correcting and preserving errors for α syndromes among the syndromes of errors with weight +1=1 are as shown in Figures 48 and 49.

[0716] When performing two-way QECCs EDP using the existing [[5,1,3]] quantum error correction code, it takes t to generate an entangled state satisfying the target fidelity of 0.95 in an environment with an initial fidelity of 0.9. corr = 0, the two-way QECCs parameter must be utilized. Therefore, if a syndrome other than 0000 is measured, the entanglement state must be discarded. However, by utilizing the present disclosure, if α = 6 syndromes (e.g., 0001, 0010, 0011, 0100, 0101, 0010) are measured, the entanglement state can be preserved and the success probability p succ =0.75484, which is a higher probability of success than the previous 0.59141. Therefore, in terms of the average resource consumed, n / (k×p succ )=5 / (1×0.75484)=6.62392, so n / (k×p succ )=5 / (1×0.59141)=8.45437, we can see a 27% reduction in resources.

[0717]

[0718] Parameter α determination technique

[0719] The parameter α is t corr The number of syndromes that will be preserved after correction without discarding the EPR state among syndromes of errors with +1 weight. (n,k,d,t corr) When two-way QECCs parameters are utilized, α is determined in the following mathematical expression 33 to consume minimum resources while satisfying the target fidelity.

[0720]

[0721] At this time F k ' means the fidelity of the output EPR state when k syndromes are additionally corrected, and can be obtained in the following mathematical expression 34.

[0722]

[0723] At this time, and is (n,k,d,t corr ) refers to the success probability and output fidelity in two-way QECCs EDP, and is (n,k,d,t corr +1) Success probability and output fidelity in two-way QECCs EDP.

[0724]

[0725] Analysis of output status

[0726] Depending on the additional error-correcting syndrome pattern and the form of the QECCs utilized, the output entangled state can be a Werner state, i.e., a Bell diagonal state W as shown in Equation 36, rather than Equation 35. Equation 35 represents a Werner state. Equation 36 represents a Bell diagonal state W where F2 = F3 = F4 is not satisfied.

[0727]

[0728]

[0729] In this case, performing additional EDP may result in degraded EDP performance, or performing additional operations utilizing the output entanglement state may result in increased computation. Therefore, if transformation into a Werner state is desired, the method of Equation 37 below can be performed.

[0730] The sender and receiver can perform twirling (ref. [2] Bennett, Charles H., et al. "Mixed-state entanglement and quantum error correction." Physical Review A 54.5 (1996): 3824.).

[0731]

[0732] Alternatively, when performing EDP thereafter, EDP can be performed by utilizing a quantum error correction code having different X, Y, and Z error correction capabilities depending on the values ​​of F2, F3, and F4. Or, U in the following mathematical expression 38 A ,U B (ref. [1] Deutsch, David, et al. "Quantum privacy amplification and the security of quantum cryptography over noisy channels." Physical review letters 77.13 (1996): 2818.) By performing this operation on both the sender and receiver, the values ​​of F2 and F4 can be swapped and the same error correction code can be used.

[0733]

[0734] Alternatively, when performing EDP thereafter, EDP can be performed using a quantum error correction code having different X, Y, and Z error correction capabilities depending on the values ​​of F2, F3, and F4.

[0735]

[0736] A process for additional resource reduction in Two-way QECCs EDP

[0737] FIG. 50 is a diagram illustrating an example of a process for performing additional resource reduction in a Two-way QECCs EDP in a system applicable to the present disclosure.

[0738] The process for additional resource reduction in Two-way QECCs EDP is as follows.

[0739] ① The sender uses two-way QECCs EDP parameters (n,k,d,t corr ) parameter as initial fidelity (F init ), goal fidelity (F target ) is estimated.

[0740] ② The sender additionally determines the number of syndromes to be corrected, α, and the α syndrome patterns.

[0741] ③ The sender (Alice) generates n EPR states using nonlinear elements.

[0742] ④ The sender transmits one qubit from each EPR state to the receiver (Bob). At this time, the quantum channel includes the generation defect of the EPR state, wired and wireless photonic channels, and quantum memory error channels.

[0743] ⑤ The sender and receiver perform the promised [[n,k,d]] quantum error correction code circuit for Unitary Operation operation as a unitary operation.

[0744] ⑥ The sender and receiver each measure (nk) qubits. At this time, the location of the measured qubit is determined based on the characteristics of the [[n,k,d]] quantum error correction code.

[0745] ⑦ The receiver transmits the measurement result of (nk) bits to the sender through the Classical Channel.

[0746] ⑧ The sender multiplies its own measurement result with the measurement result of the receiver, and performs a correction operation on the k EPR states that were not measured in the process of ④ according to the pattern of the multiplied value. At this time, the number of syndrome patterns S for which the correction operation is performed is ∑ is. The correction operation is determined as one of I, X, Y, and Z according to the pattern of s', and operates on the sender's qubit among the EPR pair states of the unmeasured EPR state. If the EPR state is discarded, steps ② to ⑧ are performed again.

[0747] ⑨ (Optional 1) To create a Werner state, the sender and receiver use Twirling or U as described above. A ,U B (Optional 2) Save F2, F3, and F4 in the Bell diagonal state and use them in the future.

[0748] For convenience of explanation, the process of preparing an EPR pair and estimating parameters at the sender (Alice) and the method of transmitting one qubit from each EPR pair from the sender to the receiver (Bob) described in this disclosure are described uniformly. However, it is self-evident that the method of preparing an EPR pair at a third-party node and transmitting it to the first node (Alice) and the second node (Bob) to perform all the methods described above, or that the same method can be applied even if the sender and receiver are changed.

[0749] In addition, it is self-evident that in the process ⑦, Alice and Bob transmit the measurement results to each other, and after each calculates the syndrome, Bob (or Alice) performs the correction operation and Alice (or Bob) can estimate the estimated value of the fidelity.

[0750] Also, the weight of the error is t as needed. reuse In the following cases, An additional protocol can be implemented to recycle the entangled state for the dog syndrome pattern.

[0751]

[0752] Quantum Teleportation (QT)

[0753] FIG. 51 is a diagram illustrating an example of quantum teleportation (QT) in a system applicable to the present disclosure.

[0754] Quantum teleportation (QT) is a technology that transmits quantum information from a sender at a specific location to a receiver located a certain distance away. Contrary to the original meaning of the word "teleport," in quantum teleportation, the carriers on both sides are fixed, and the actual carriers are not transferred, but quantum information is transferred between carriers. This information transfer requires an entangled quantum state, or Bell State, which provides statistical correlation between separate physical systems. Since any change experienced by one of the entangled particles causes the other particle to undergo the same change, the two particles behave as if they were in a single quantum state.

[0755] Figure 51 schematically illustrates a quantum transmission protocol using photons. Quantum transmission requires the resources of a classical channel capable of transmitting two classical bits, a Bell State (entanglement state) generation device, a quantum channel for transporting two particles in the Bell State to different locations of the transmitter and receiver, a Bell State Measurement device at the transmitter, and a Unitary Operation device at the receiver. The quantum information to be transmitted The operation of the protocol is as follows:

[0756] (1) Entanglement Generation: An entanglement state of two qubits is created using a Bell State generation device.

[0757] (2) Entanglement Distribution: The generated entanglement state is transferred through a quantum channel, with one qubit transferred to the location of the sender Alice (A) and the other qubit transferred to the location of the receiver Bob (B).

[0758] (3) Quantum Pre-processing: Alice wants to transmit the quantum state And performs Bell State Measurement on one of the qubits in the Bell State that he has, and obtains a result corresponding to one of the four Bell States. At this time, the state of the qubit that Bob has changes according to the result of Alice's Bell State Measurement, as shown in Table 20 below. Table 20 shows the change in Bob's qubit according to the result of Alice's Bell State Measurement.

[0759]

[0760] (4) Classical Transmission: Alice encodes the Bell State Measurement result into two classical bits and transmits it to Bob through the classical channel.

[0761] (5) Quantum Post-processing: Based on the two bits of information received from Alice, Bob performs a unitary operation on the remaining qubit of the Bell State he has to obtain the quantum information that Alice wanted to transmit. Obtain the same quantum state as .

[0762]

[0763] Quantum Direct Communication (QDC)

[0764] FIG. 52 is a diagram illustrating an example of a two-step QSDC protocol in a system applicable to the present disclosure.

[0765] Quantum direct communication shares a commonality with quantum key distribution (QKD), a 4 / 5G secure communication technology, in that both techniques securely transmit classical message information. However, QKD utilizes the quantum mechanical property of unclonability to share symmetric secret keys between senders and receivers via a quantum channel, whereas QDC shares the classical message information directly via the quantum channel, rather than the secret key.

[0766] The QDC technology family includes quantum secure direct communication (QSDC), which has the advantage of ensuring high security by not generating information leakage related to transmitted information, and includes a two-step QSDC technique that largely utilizes an entangled light source.

[0767] Two-step QSDC is a technique derived from super dense coding, as shown in Fig. 52, and is a technique for safely transmitting 2 bits of classical information using four types of single entangled photons (EPR-pairs) of mathematical formula 39 below.

[0768]

[0769] Superdense coding is a technique that enables classical information to be transmitted using quantum communication. Using superdense coding, a transmitter can transmit two bits of classical information to a distant receiver using a single qubit through a quantum channel. When using superdense coding, the transmitter is assumed to possess the first entangled qubit, and the receiver is assumed to possess the second entangled qubit. The qubit that the transmitter wishes to transmit can be in one of four cases: '00', '01', '10', and '11'. For each of the four cases, the transmitter performs a qubit operation (expressed in the form of I, Z, X, iY) corresponding to each of the four cases on the entangled qubits it owns, and then transmits the information through the quantum channel. Each operation performed by the transmitter can be understood as transforming the entangled state shared by the transmitter and receiver into a different basis that is orthogonal to each other. The receiver measures the received qubit and its own qubit (the second qubit in the entangled state) to restore the two bits of information transmitted by the transmitter.

[0770] In Fig. 52, SR (Storage lines) 1 to 4 are optical delay lines that serve as quantum memories, CE (Checking Eavesdropping) 1 and 2 check for the presence of an eavesdropper, CM (Coding Message) encodes classical message information to be transmitted from the transmitter (Alice) to the receiver (Bob), EPR-source generates an entangled light source, and Bell state measurement measures an entangled photon pair.

[0771] In two-step QSDC, unlike super dense coding, the entangled photon pair is not transmitted all at once, but is transmitted in two steps through an upper quantum channel and a lower quantum channel. In order to eavesdrop on the entangled light source, information on both sides of the entangled photon pair must be known to determine the transmitted information through measurement. Therefore, in the two-step technique, one side of the entangled photon pair is sent first to verify its safety from eavesdropping, and only when safety is guaranteed is the message information to be sent coded and transmitted on the remaining part of the photon pair.

[0772]

[0773] Composition of various embodiments of the present disclosure

[0774] This disclosure proposes a technique for reusing entangled pairs, considering that the fidelity of previously discarded entangled pairs can be additionally utilized even though it is lower than the target fidelity when performing EDP using two-way QECCs utilized in adaptive mode. The criterion for reusing entangled pairs can be in various forms, but as an example, the fidelity (F') of an unmeasured qubit is assumed to be the initial fidelity (F input ) is shown in higher cases.

[0775]

[0776] The symbols / abbreviations / terms used in this disclosure are as follows.

[0777] - EDP: Entanglement Distillation Protocol

[0778] - LOCC: Local Operator and Classical Communication

[0779] - QSDC: Quantum Secure Direct Communication

[0780] - F: Fidelity

[0781] - p_succ: Success probability

[0782] - W,W',W'': Bell diagonal state

[0783] - W F : Werner state

[0784]

[0785] Technical Problem to be Solved by the Invention

[0786] Protocol performance differences depending on input Bell diagonal state

[0787] The recurrence method performs the following Twirling operation in each round. Twirling is an operator that transforms the Bell diagonal state W of Equation 40 into the Werner state W_F.

[0788]

[0789] That is, the Twirling operation is In W state It is an operator that creates a Werner state. In the recurrence protocol, after twirling, In W F To make it so, Alice or Bob applies the Y operator.

[0790] Bell diagonal states other than the Werner states described above can be generated in two-way QECCs EDP depending on the choice of the conservation syndrome, or when the EPR state passes through an asymmetric depolarizing channel rather than a symmetric depolarizing channel.

[0791] The QPA protocol is a unitary operation U of the following mathematical formula 41. A ,U B is performed in each round. The corresponding Unitary operation transforms the Bell diagonal state into a Bell diagonal state with different probability.

[0792]

[0793] At this time am.

[0794] Also, in the Recurrence or QPA protocol, Twirling or the Unitary operation of the above mathematical expression 41, U A ,U B The states generated through the subsequent CNOT operation and the operation of measuring the entanglement state of the target qubit of the CNOT with each Z basis are each identical. The entanglement state is as shown in the mathematical expression 42 below. It is transformed into .

[0795]

[0796] At this time p succ is defined as in the mathematical expression 43 below, and F' is is defined as

[0797]

[0798] So initial W, W F Or according to the form of W' (W,W F In the case of F, according to F, in the case of W', according to F,F1,F2,F3) generated Fidelity F' and success probability p succ will have different values.

[0799] If W' is a Werner state with the same fidelity (F) as W, the performance of QPA and Recurrence protocols are the same when a single round is performed, but Twirling and UA ,U B Due to the differences in computation, QPA offers superior fidelity and success probability performance. Table 21 below shows the performance of each protocol when executing two rounds with a Werner state as the input state.

[0800] ProtocolFF1F2F3F'F'1F2F3'p_succ'QPA0.70.10.10.10.8459460.0189190.0675680.0 675680.640138Recurrence0.70.10.10.10.7731710.0219510.0219510.1829270.482353

[0801] In Table 21, 'Protocol' is the type of protocol, "F","F1","F2","F3" are the values ​​of F, F1, F2, F3 in W, W', and "F'","F1'","F2'","F3'" are the output status ( ) values ​​of F, F1, F2, F3, “p_succ” is p succ It means.

[0802] If W' has the same fidelity (F) as W but is a Bell diagonal state rather than a Werner state, the result of performing a single round may be that QPA or the recurrence protocol may show superior performance depending on F1, F2, and F3 that constitute W'. Table 22 below shows the performance of the QPA and recurrence protocols according to the input states (W, W'').

[0803] ProtocolFF1F2F3F'F'1F2F3'p_succ'QPA0.650.20.10.050.6208050.0134230.0167790.3489930.7450.650.20 .050.10.6208050.0134230.0167790.3489930.7450.650.10.20.050.6920.0320.0680.2080.6250.650.10.050. 20.6920.0320.0680.2080.6250.650.050.20.10.7327590.0689660.0862070.1120690.580.650.050.10.20.732 7590.0689660.0862070.1120690.58Recurrence0.650.10.050.20.6790660.0423880.0423880.2361590.642222

[0804]

[0805] In Table 22, 'Protocol' is the type of protocol, "F","F1","F2","F3" are the values ​​of F, F1, F2, F3 in W, W', and "F'","F1'","F2'","F3'" are the output status ( ) values ​​of F, F1, F2, F3, “p_succ” is p succ It means.

[0806] That is, even if the CNOT operation being performed and the measurement qubit are the same, the LOCC (U) performed prior to the operation A ,U B Or Twirling,Y A ) may result in differences in protocol performance. In other words, the LOCC operation can improve the performance of the desired protocol by appropriately changing the input state.

[0807]

[0808] Detailed Description of the Invention

[0809] How to transform Bell Diagonal state

[0810] This technique presents a method for transforming Bell diagonal states. Bell diagonal states can be transformed into each unitary state as follows:

[0811] ① How to use

[0812]

[0813] At this time is. Through this operation, While maintaining and It can perform conversions between.

[0814] ② How to use

[0815]

[0816] At this time is. Through this operation, While maintaining It can perform conversions between.

[0817] ③ How to use

[0818]

[0819] At this time is. Through this operation, While maintaining It can perform conversions between.

[0820] ④ How to use

[0821]

[0822] At this time is. Through this operation, While maintaining and It can perform conversions between.

[0823] and Operators can be used to freely transform Bell diagonal states. Additionally, Twirling and Pauli Y can be used to transform Bell diagonal states into Werner states.

[0824] Also, considering the global phase, in front of each unitary is included It is self-evident that the same result can be obtained even if .

[0825]

[0826] 2-1 EDP using operator adaptation

[0827] FIG. 53 is a diagram illustrating an example of an EDP performing circuit after a transformation process of a Bell diagonal state utilizing various unitary operations in a system applicable to the present disclosure.

[0828] FIG. 54 is a diagram illustrating an example of a recurrence protocol (an EDP performing circuit after changing the Werner state by twirling) in a system applicable to the present disclosure.

[0829] As described above in [Performance difference of protocol according to input Bell diagonal state], the Z basis measurement of CNOT and target qubits changes the state as shown in Equation 48 below.

[0830]

[0831] Therefore, in this technique, the target p succ Or We propose a technique to perform appropriate unitary operations according to the

[0832] That is, the transmitter and receiver are fixed as in the embodiment of Fig. 29. Rather than performing it, it is tailored to the target performance. We propose an operator adaptation technique that performs (the embodiment of Fig. 53) or Twirling (the embodiment of Fig. 54). Additionally, due to the characteristics of 2-1 EDP performance, Alternatively, choosing between Twirling may suffice.

[0833] For example, the initial state is In this case, the QPA protocol always p succ =0.625 probability and can only generate Bell diagonal states with fidelity F'=0.692, and recurrence is always p succ =0.64 probability can be used to generate a Bell diagonal state with fidelity F'=0.67. However, using this technique, p succ =0.58 probability can generate a Bell diagonal state with fidelity F'=0.733.

[0834] Also, the initial state is In this case, if the QPA protocol cannot provide improved performance, this technique can be used. By using p succ =0.58 probability to generate a Bell diagonal state with fidelity F'=0.733. Using different Unitaries, it is possible to generate states with different fidelities and different success probabilities.

[0835]

[0836] FIG. 55 is a diagram illustrating an example of a process for performing a 2-1 EDP protocol utilizing operator adaptation in a system applicable to the present disclosure.

[0837] The execution process of the 2-1 EDP protocol according to Fig. 55 is as follows.

[0838] ① Determine the coefficients of the W' state, F, F1, F2, and F3, through channel estimation.

[0839] ② The sender is in accordance with the target output state. Or choose the appropriate operator O among Twirling

[0840] ③ The sender creates two EPR states.

[0841] ④ The sender transmits one qubit from each EPR state to the receiver (Bob) (2 in total). The transmitted qubit undergoes a quantum channel, which includes the generation defect of the EPR state, the wired and wireless photonic channel, and the quantum memory error channel.

[0842] ⑤ The sender and receiver perform operations on each qubit according to the operator O determined in process ②.

[0843] ⑥ Two entangled states are gathered and bundled, and the CNOT and CNOT's target qubits are measured in the Z basis.

[0844] ⑦ The transmitter and receiver share the Z-based measurement results.

[0845] ⑧ Compare whether the measurement results are the same. If they are the same, the entanglement state used as the control qubit is preserved. If they are different, the entanglement state used as the control qubit is discarded and process ③ is started again.

[0846] The method of preparing an EPR pair at a sender (Alice) and transmitting one qubit from each EPR pair to a receiver (Bob) described in the present invention is described uniformly for convenience of explanation. However, it is self-evident that the same method can be applied to a method of preparing an EPR pair at a third-party node and transmitting it to the first node (Alice) and the second node (Bob) to perform all of the methods described above.

[0847] In addition, in the present invention, although the same W^' state is considered as an input value, in order to generate the target output state for different Bell diagonal states, Alternatively, it is obvious that one can choose an appropriate operator O among Twirling.

[0848]

[0849] Effects of various embodiments of the present disclosure

[0850] The expected effects of various embodiments of the present disclosure are as follows.

[0851] In this disclosure, a method for transforming a Bell diagonal state is proposed.

[0852] According to various embodiments of the present disclosure, an operator adaptation method, which is a technique for performing Twirling according to a target performance, is proposed.

[0853]

[0854] Characteristic configurations of various embodiments of the present disclosure are as follows.

[0855] (1) As a method of performing the 2-1 EDP protocol

[0856] How to design an operator that changes the state of a Bell diagonal;

[0857] How to choose an appropriate operator based on a given Bell diagonal state;

[0858] How to perform the EDP protocol using selected and additional operators.

[0859]

[0860] Through the above configuration, the present disclosure proposes an EDP protocol that can increase efficiency in the 2-1 EDP protocol.

[0861]

[0862] [Description of the first node claim]

[0863] The embodiments described below are specifically described with reference to FIG. 56 in terms of the operation of the first node. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method, as long as they are not mutually exclusive.

[0864] FIG. 56 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0865] According to various embodiments of the present disclosure, a method performed by a first node in a communication system is provided.

[0866] According to various embodiments of the present disclosure, the first node and the second node may be included in a plurality of nodes. According to various embodiments of the present disclosure, each of the plurality of nodes may correspond to either a terminal or a base station in a wireless communication system.

[0867] The embodiment of FIG. 56 may further include, before step S5601, one or more of the following steps: a step in which the first node receives one or more synchronization signals from the second node; a step in which the first node receives system information from the second node; a step in which the first node receives configuration information from the second node; and a step in which the first node receives control information from the second node.

[0868] The embodiment of FIG. 56 may further include, before step S5601, one or more of the following steps: a step in which the first node transmits a random access preamble to the second node; a step in which the first node receives a random access response (RAR) from the second node; a step in which the first node transmits a random access message 3 to the second node; and a step in which the first node receives a contention resolution message from the second node. Message 3 is a first PUSCH transmission scheduled by the RAR together with an RAR UL grant.

[0869] In step S5601, the first node determines a plurality of coefficients for a Bell diagonal state through estimation of a channel between the first node and the second node.

[0870] At step S5602, the first node determines an operator for transforming the Bell diagonal state into a Werner state or another Bell diagonal state with different coefficients based on the target output state.

[0871] At step S5603, the first node performs EDP (entanglement distillation protocol) based on the above operator.

[0872] In step S5604, the first node preserves one or more EPR (Einstein-Podolsky-Rosen) states related to the execution result of the EDP based on the execution result of the EDP, or discards one or more EPR states related to the execution result of the EDP and repeatedly executes the EDP.

[0873]

[0874] According to various embodiments of the present disclosure, the operator may be determined based on the target output state among a first operator corresponding to one of a plurality of unitary operations or a second operator corresponding to a twirling operation.

[0875] According to various embodiments of the present disclosure, the target output state may correspond to a success probability that the bell diagonal state can have target fidelity, or the target fidelity.

[0876] According to various embodiments of the present disclosure, in the embodiment of FIG. 56, the step S5603 of performing the EDP based on the operator may include: generating a set number of first EPR states; transmitting one of the two qubits constituting each of the first EPR states to the second node; generating a first measurement result by performing an operation based on the operator on second qubits corresponding to some of the first qubits associated with the first EPR states; transmitting the first measurement result to the second node; and receiving a second measurement result for the second qubits from the second node.

[0877] According to various embodiments of the present disclosure, in the embodiment of FIG. 56, the step S5604 of preserving one or more EPR states related to the performance result of the EDP or discarding one or more EPR states related to the performance result of the EDP and repeatedly performing the EDP based on the performance result of the EDP may include the steps of: preserving one or more third EPR states among the first EPR states when the first measurement result and the second measurement result match; and discarding one or more third EPR states among the first EPR states and repeatedly performing the EDP when the first measurement result and the second measurement result do not match.

[0878] According to various embodiments of the present disclosure, the first measurement result and the second measurement result can be generated by performing a unitary operation or a twirling operation based on the operator on the second qubits, followed by performing a CNOT operation (controlled NOT operation) and a Z basis measurement on the target qubit of the CNOT operation.

[0879] According to various embodiments of the present disclosure, the one or more third EPR states may be EPR states corresponding to a control qubit.

[0880]

[0881] According to various embodiments of the present disclosure, a first node is provided in a communication system. The first node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the first node according to FIG. 56.

[0882]

[0883] According to various embodiments of the present disclosure, a device for controlling a first node in a communication system is provided. The device includes at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing an operating method of the first node according to FIG. 56 based on instructions executed by the at least one processor.

[0884]

[0885] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of the first node according to FIG. 56.

[0886]

[0887] [Description of the second node claim]

[0888] The embodiments described below are specifically described with reference to FIG. 57 in terms of the operation of the second node. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for some components of another method, or may be applied in combination with each other, as long as they are not mutually exclusive.

[0889] FIG. 57 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0890] According to various embodiments of the present disclosure, a method performed by a second node in a communication system is provided.

[0891] According to various embodiments of the present disclosure, the first node and the second node may be included in a plurality of nodes. According to various embodiments of the present disclosure, each of the plurality of nodes may correspond to either a terminal or a base station in a wireless communication system.

[0892] The embodiment of FIG. 57 may further include, before step S5701, one or more of the following steps: a step in which the second node transmits one or more synchronization signals to the first node; a step in which the second node transmits system information to the first node; a step in which the second node transmits configuration information to the first node; and a step in which the second node transmits control information to the first node.

[0893] The embodiment of FIG. 57 may further include, before step S5701, one or more of the following steps: a step in which the second node receives a random access preamble from the first node; a step in which the second node transmits a random access response (RAR) to the first node; a step in which the second node receives a random access message 3 from the first node; and a step in which the second node transmits a contention resolution message to the first node. Message 3 is a first PUSCH transmission scheduled by RAR together with an RAR UL grant.

[0894] In step S5701, the second node determines a plurality of coefficients for a Bell diagonal state through estimation of the channel between the first node and the second node.

[0895] At step S5702, the second node determines an operator for transforming the Bell diagonal state into a Werner state or another Bell diagonal state with different coefficients based on the target output state.

[0896] At step S5703, the second node performs EDP (entanglement distillation protocol) based on the above operator.

[0897] In step S5704, the second node preserves one or more EPR (Einstein-Podolsky-Rosen) states related to the execution result of the EDP based on the execution result of the EDP, or discards one or more EPR states related to the execution result of the EDP and repeatedly executes the EDP.

[0898]

[0899] According to various embodiments of the present disclosure, the operator may be determined based on the target output state among a first operator corresponding to one of a plurality of unitary operations or a second operator corresponding to a twirling operation.

[0900] According to various embodiments of the present disclosure, the target output state may correspond to a success probability that the bell diagonal state can have target fidelity, or the target fidelity.

[0901] According to various embodiments of the present disclosure, in the embodiment of FIG. 57, the step S5703 of performing the EDP based on the operator may include: receiving one of two qubits constituting each of a set number of first EPR states from the first node; generating a second measurement result by performing an operation based on the operator on second qubits corresponding to some of the first qubits associated with the first EPR states; transmitting the first measurement result for the second qubits from the first node; and transmitting the second measurement result for the second qubits to the second node.

[0902] According to various embodiments of the present disclosure, in the embodiment of FIG. 57, the step S5704 of preserving one or more EPR states related to the performance result of the EDP or discarding one or more EPR states related to the performance result of the EDP and repeatedly performing the EDP based on the performance result of the EDP may include the steps of: preserving one or more third EPR states among the first EPR states when the first measurement result and the second measurement result match; and discarding one or more third EPR states among the first EPR states and repeatedly performing the EDP when the first measurement result and the second measurement result do not match.

[0903] According to various embodiments of the present disclosure, the first measurement result and the second measurement result can be generated by performing a unitary operation or a twirling operation based on the operator on the second qubits, followed by performing a CNOT operation (controlled NOT operation) and a Z basis measurement on the target qubit of the CNOT operation.

[0904] According to various embodiments of the present disclosure, the one or more third EPR states may be EPR states corresponding to a control qubit.

[0905]

[0906] According to various embodiments of the present disclosure, a second node is provided in a communication system. The second node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the second node according to FIG. 57.

[0907]

[0908] According to various embodiments of the present disclosure, a device for controlling a second node in a communication system is provided. The device includes at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing an operating method of the second node according to FIG. 57 based on instructions executed by the at least one processor.

[0909]

[0910] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of a second node according to FIG. 57.

[0911]

[0912] Communication system applicable to the present disclosure

[0913] FIG. 58 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0914] Referring to FIG. 58, a communication system (1) applicable to various embodiments of the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution), 6G wireless communication), and may be referred to as a communication / wireless / 5G device / 6G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. Mobile devices may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. Home appliances may include a TV, a refrigerator, a washing machine, etc. IoT devices may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may act as a base station / network node to other wireless devices.

[0915] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0916] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of various embodiments of the present disclosure.

[0917] Meanwhile, NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, an SCS of 15 kHz supports a wide area in traditional cellular bands; an SCS of 30 kHz / 60 kHz supports dense urban areas, lower latency, and wider carrier bandwidth; and an SCS of 60 kHz or higher supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0918] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges can be changed, and for example, the frequency ranges of the two types (FR1, FR2) can be as shown in Table 23 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 can mean the "sub 6 GHz range", and FR2 can mean the "above 6 GHz range" and can be called millimeter wave (mmW).

[0919]

[0920] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0921] As described above, the numerical value of the frequency range of the NR system can be changed. For example, FR1 may include a band from 410 MHz to 7125 MHz, as shown in Table 24 below. That is, FR1 may include a frequency band above 6 GHz (or 5850, 5900, 5925 MHz, etc.). For example, the frequency band above 6 GHz (or 5850, 5900, 5925 MHz, etc.) included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, such as for vehicular communications (e.g., autonomous driving).

[0922] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0923]

[0924] According to various embodiments of the present disclosure, the communication system (1) can support terahertz (THz) wireless communication. THz wireless communication is a wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band where propagation loss due to absorption of molecules in the air is small.

[0925]

[0926] Wireless devices applicable to the present disclosure

[0927] Below, examples of wireless devices to which various embodiments of the present disclosure are applied are described.

[0928] FIG. 59 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0929] Referring to FIG. 59, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 58.

[0930] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). In addition, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In various embodiments of the present disclosure, a wireless device may mean a communication modem / circuit / chip.

[0931] The second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). Furthermore, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In various embodiments of the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0932] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0933] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0934] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0935] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0936] FIG. 60 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0937] According to FIG. 60, the wireless device may include at least one processor (102, 202), at least one memory (104, 204), at least one transceiver (106, 206), and one or more antennas (108, 208).

[0938] The difference between the example of the wireless device described in FIG. 59 and the example of the wireless device in FIG. 60 is that in FIG. 59, the processor (102, 202) and the memory (104, 204) are separated, but in the example of FIG. 60, the memory (104, 204) is included in the processor (102, 202).

[0939] Here, the specific description of the processor (102, 202), memory (104, 204), transceiver (106, 206), and one or more antennas (108, 208) is as described above, so in order to avoid unnecessary repetition of description, the description of the repeated description is omitted.

[0940] Below, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0941] Figure 61 illustrates a signal processing circuit for a transmission signal.

[0942] Referring to FIG. 61, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operations / functions of FIG. 61 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 59. The hardware elements of FIG. 61 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 59. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 59. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 59, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 59.

[0943] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 61. Here, the codeword is an encoded bit sequence of an information block. The information block can include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0944] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0945] The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (1060) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0946] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 61. For example, a wireless device (e.g., 100, 200 of FIG. 59) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0947] Below, examples of wireless device utilization to which various embodiments of the present disclosure are applied are described.

[0948] Figure 62 illustrates another example of a wireless device applicable to various embodiments of the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 58).

[0949] Referring to FIG. 62, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 59 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and an additional element (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 59. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 59. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0950] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 58, 100a), a vehicle (Fig. 58, 100b-1, 100b-2), an XR device (Fig. 58, 100c), a portable device (Fig. 58, 100d), a home appliance (Fig. 58, 100e), an IoT device (Fig. 58, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 58, 400), a base station (Fig. 58, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0951] In FIG. 62, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and a first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of a set of one or more processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0952] Below, an implementation example of Fig. 62 is described in more detail with reference to the drawings.

[0953] Figure 63 illustrates a mobile device applicable to various embodiments of the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smartglasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT).

[0954] Referring to FIG. 63, the portable device (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a memory unit (130), a power supply unit (140a), an interface unit (140b), and an input / output unit (140c). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 62, respectively.

[0955] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control components of the mobile device (100) to perform various operations. The control unit (120) can include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / codes / commands required for operating the mobile device (100). In addition, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the mobile device (100) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the mobile device (100) and other external devices. The interface unit (140b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (140c) may include a camera, a microphone, a user input unit, a display unit (140d), a speaker, and / or a haptic module.

[0956] For example, in the case of data communication, the input / output unit (140c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0957] FIG. 64 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0958] Vehicles or autonomous vehicles can be implemented as mobile robots, cars, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0959] Referring to FIG. 64, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 62, respectively.

[0960] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0961] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0962] Figure 65 illustrates a vehicle applicable to various embodiments of the present disclosure. The vehicle may also be implemented as a means of transportation, a train, an aircraft, a ship, or the like.

[0963] Referring to FIG. 65, the vehicle (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), and a position measurement unit (140b). Here, blocks 110 to 130 / 140a to 140b correspond to blocks 110 to 130 / 140 of FIG. 62, respectively.

[0964] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other vehicles or external devices such as base stations. The control unit (120) can control components of the vehicle (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (140a) can output AR / VR objects based on information in the memory unit (130). The input / output unit (140a) can include a HUD. The position measurement unit (140b) can obtain position information of the vehicle (100). The position information can include absolute position information of the vehicle (100), position information within a driving line, acceleration information, position information with respect to surrounding vehicles, etc. The position measurement unit (140b) can include GPS and various sensors.

[0965] For example, the communication unit (110) of the vehicle (100) can receive map information, traffic information, etc. from an external server and store them in the memory unit (130). The location measurement unit (140b) can obtain vehicle location information through GPS and various sensors and store the information in the memory unit (130). The control unit (120) can create a virtual object based on the map information, traffic information, and vehicle location information, and the input / output unit (140a) can display the created virtual object on the vehicle window (1410, 1420). In addition, the control unit (120) can determine whether the vehicle (100) is being driven normally within the driving line based on the vehicle location information. If the vehicle (100) abnormally deviates from the driving line, the control unit (120) can display a warning on the vehicle window through the input / output unit (140a). Additionally, the control unit (120) can broadcast a warning message regarding driving abnormalities to surrounding vehicles through the communication unit (110). Depending on the situation, the control unit (120) can transmit vehicle location information and information regarding driving / vehicle abnormalities to relevant authorities through the communication unit (110).

[0966] Figure 66 illustrates an XR device applicable to various embodiments of the present disclosure. The XR device may be implemented as an HMD, a head-up display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, and the like.

[0967] Referring to FIG. 66, the XR device (100a) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a power supply unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 62, respectively.

[0968] The communication unit (110) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. The media data can include videos, images, sounds, etc. The control unit (120) can control components of the XR device (100a) to perform various operations. For example, the control unit (120) can be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation and processing, etc. The memory unit (130) can store data / parameters / programs / codes / commands required for driving the XR device (100a) / generating XR objects. The input / output unit (140a) can obtain control information, data, etc. from the outside, and output the generated XR objects. The input / output unit (140a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module, etc. The sensor unit (140b) can obtain the XR device status, surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar. The power supply unit (140c) supplies power to the XR device (100a) and may include a wired / wireless charging circuit, a battery, etc.

[0969] For example, the memory unit (130) of the XR device (100a) may include information (e.g., data, etc.) required for creating an XR object (e.g., AR / VR / MR object). The input / output unit (140a) may obtain a command to operate the XR device (100a) from the user, and the control unit (120) may operate the XR device (100a) according to the user's operating command. For example, when a user attempts to watch a movie, news, etc. through the XR device (100a), the control unit (120) may transmit content request information to another device (e.g., a mobile device (100b)) or a media server through the communication unit (130). The communication unit (130) may download / stream content such as movies and news from another device (e.g., a mobile device (100b)) or a media server to the memory unit (130). The control unit (120) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for content, and can generate / output an XR object based on information about surrounding space or real objects acquired through the input / output unit (140a) / sensor unit (140b).

[0970] In addition, the XR device (100a) is wirelessly connected to the mobile device (100b) through the communication unit (110), and the operation of the XR device (100a) can be controlled by the mobile device (100b). For example, the mobile device (100b) can act as a controller for the XR device (100a). To this end, the XR device (100a) can obtain three-dimensional position information of the mobile device (100b), and then generate and output an XR object corresponding to the mobile device (100b).

[0971] Figure 67 illustrates robots applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, household, military, and other categories depending on their intended use or field.

[0972] Referring to FIG. 67, the robot (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a driving unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 62, respectively.

[0973] The communication unit (110) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (120) can control components of the robot (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the robot (100). The input / output unit (140a) can obtain information from the outside of the robot (100) and output information to the outside of the robot (100). The input / output unit (140a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (140b) can obtain internal information of the robot (100), surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit (140c) may perform various physical operations such as moving the robot joints. In addition, the driving unit (140c) may enable the robot (100) to drive on the ground or fly in the air. The driving unit (140c) may include an actuator, a motor, wheels, brakes, propellers, etc.

[0974] FIG. 68 illustrates an AI device applicable to various embodiments of the present disclosure.

[0975] AI devices can be implemented as fixed or mobile devices, such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, and vehicles.

[0976] Referring to FIG. 68, the AI ​​device (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a / 140b), a learning processor unit (140c), and a sensor unit (140d). Blocks 110 to 130 / 140a to 140d correspond to blocks 110 to 130 / 140 of FIG. 62, respectively.

[0977] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., FIG. 58, 100x, 200, 400) or AI servers (200) using wired and wireless communication technology. To this end, the communication unit (110) can transmit information within the memory unit (130) to the external device or transfer a signal received from the external device to the memory unit (130).

[0978] The control unit (120) may determine at least one executable operation of the AI ​​device (100) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (120) may control components of the AI ​​device (100) to perform the determined operation. For example, the control unit (120) may request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and may control components of the AI ​​device (100) to perform at least one executable operation, a predicted operation, or an operation determined to be desirable. In addition, the control unit (120) may collect history information including the operation details of the AI ​​device (100) or user feedback on the operation, and store the collected history information in the memory unit (130) or the learning processor unit (140c), or transmit the collected history information to an external device such as an AI server (FIG. 58, 400). The collected history information may be used to update a learning model.

[0979] The memory unit (130) can store data that supports various functions of the AI ​​device (100). For example, the memory unit (130) can store data obtained from the input unit (140a), data obtained from the communication unit (110), output data of the learning processor unit (140c), and data obtained from the sensing unit (140). In addition, the memory unit (130) can store control information and / or software codes necessary for the operation / execution of the control unit (120).

[0980] The input unit (140a) can obtain various types of data from the outside of the AI ​​device (100). For example, the input unit (120) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (140a) may include a camera, a microphone, and / or a user input unit. The output unit (140b) may generate output related to sight, hearing, or touch. The output unit (140b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (140) can obtain at least one of internal information of the AI ​​device (100), information about the surrounding environment of the AI ​​device (100), and user information using various sensors. The sensing unit (140) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar, etc.

[0981] The learning processor unit (140c) can train a model composed of an artificial neural network using learning data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 58, 400). The learning processor unit (140c) can process information received from an external device via the communication unit (110) and / or information stored in the memory unit (130). In addition, the output value of the learning processor unit (140c) can be transmitted to an external device via the communication unit (110) and / or stored in the memory unit (130).

[0982] The claims described in the various embodiments of the present disclosure may be combined in various ways. For example, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the device claims of the various embodiments of the present disclosure may be combined and implemented as a method. Furthermore, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a method.

Claims

1. In the operation method of the first node in the communication system, A step of receiving at least one synchronization signal from a second node; A step of receiving a control signal (control information) from the second node; A step of determining a plurality of coefficients for a Bell diagonal state through estimation of a channel between the first node and the second node; A step of determining an operator for converting the Bell diagonal state into a Werner state or another Bell diagonal state having different coefficients based on the target output state; A step of performing EDP (entanglement distillation protocol) based on the above operator; and A step of preserving one or more EPR (Einstein-Podolsky-Rosen) states related to the execution result of the EDP based on the execution result of the EDP, or discarding one or more EPR states related to the execution result of the EDP and repeatedly executing the EDP, method.

2. In paragraph 1, The above operator is determined based on the target output state among the first operator corresponding to one of the plurality of unitary operations or the second operator corresponding to the twirling operation. method.

3. In paragraph 1, The above target output state is the success probability that the bell diagonal state can have the target fidelity, or, corresponding to the target fidelity, method.

4. In paragraph 1, The step of performing the EDP based on the above operator is: A step of generating a set number of first EPR states; A step of transmitting one of the two qubits constituting each of the first EPR states to the second node; A step of generating a first measurement result by performing an operation based on the operator on second qubits corresponding to some of the first qubits associated with the first EPR states; A step of transmitting the first measurement result to the second node; comprising a step of receiving a second measurement result for the second qubits from the second node; method.

5. In paragraph 4, Based on the execution result of the EDP, the step of preserving one or more EPR states related to the execution result of the EDP or discarding one or more EPR states related to the execution result of the EDP and repeatedly executing the EDP is as follows: A step of preserving at least one third EPR state among the first EPR states when the first measurement result and the second measurement result match; Including a step of discarding one or more third EPR states among the first EPR states and repeatedly performing the EDP when the first measurement result and the second measurement result do not match, method.

6. In paragraph 5, The above first measurement result and the above second measurement result, After performing a unitary operation or a twirling operation based on the operator on the second qubits, a CNOT operation (controlled NOT operation) and a Z basis measurement on the target qubit of the CNOT operation are generated. method.

7. In paragraph 6, The one or more third EPR states are EPR states corresponding to the control qubit. method.

8. In the method of operation of the second node in the communication system, A step of transmitting at least one synchronization signal to a first node; A step of transmitting a control signal (control information) to the first node; A step of determining a plurality of coefficients for a Bell diagonal state through estimation of a channel between the first node and the second node; A step of determining an operator for converting the Bell diagonal state into a Werner state or another Bell diagonal state having different coefficients based on the target output state; A step of performing EDP (entanglement distillation protocol) based on the above operator; and A step of preserving one or more EPR (Einstein-Podolsky-Rosen) states related to the execution result of the EDP based on the execution result of the EDP, or discarding one or more EPR states related to the execution result of the EDP and repeatedly executing the EDP, method.

9. In paragraph 8, The above operator is determined based on the target output state among the first operator corresponding to one of the plurality of unitary operations or the second operator corresponding to the twirling operation. method.

10. In paragraph 9, The above target output state is the success probability that the bell diagonal state can have the target fidelity, or, corresponding to the target fidelity, method.

11. In paragraph 8, The step of performing the EDP based on the above operator is: A step of receiving one of the two qubits constituting each of the set number of first EPR states from the first node; A step of generating a second measurement result by performing an operation based on the operator on second qubits corresponding to some of the first qubits associated with the first EPR states; A step of transmitting a first measurement result for the second qubits from the first node; comprising a step of transmitting a second measurement result for the second qubits to the second node; method.

12. In paragraph 11, Based on the execution result of the EDP, the step of preserving one or more EPR states related to the execution result of the EDP or discarding one or more EPR states related to the execution result of the EDP and repeatedly executing the EDP is as follows: A step of preserving at least one third EPR state among the first EPR states when the first measurement result and the second measurement result match; Including a step of discarding one or more third EPR states among the first EPR states and repeatedly performing the EDP when the first measurement result and the second measurement result do not match, method.

13. In paragraph 12, The above first measurement result and the above second measurement result, After performing a unitary operation or a twirling operation based on the operator on the second qubits, a CNOT operation (controlled NOT operation) and a Z basis measurement on the target qubit of the CNOT operation are generated. method.

14. In paragraph 13, The one or more third EPR states are EPR states corresponding to the control qubit. method.

15. In the first node of the communication system, Transmitter and receiver; at least one processor; and At least one memory operably connectable to said at least one processor and storing instructions that, when executed by said at least one processor, perform operations; The above actions are, Comprising all steps of the method according to any one of claims 1 to 7, Node 1.

16. In the second node of the communication system, Transmitter and receiver; at least one processor; and At least one memory operably connectable to said at least one processor and storing instructions that, when executed by said at least one processor, perform operations; The above actions are, Comprising all steps of the method according to any one of claims 8 to 14, Second node.

17. In a control device that controls a first node in a communication system, at least one processor; and comprising at least one memory operably connected to at least one of the processors; The at least one memory stores instructions for performing operations based on being executed by the at least one processor, The above actions are, Comprising all steps of the method according to any one of claims 1 to 7, controller.

18. In a control device that controls a second node in a communication system, at least one processor; and comprising at least one memory operably connected to at least one of the processors; The at least one memory stores instructions for performing operations based on being executed by the at least one processor, The above actions are, Comprising all steps of the method according to any one of claims 8 to 14, controller.

19. In one or more non-transitory computer-readable media storing one or more instructions, The one or more instructions perform operations based on being executed by one or more processors, The above actions are, Comprising all steps of the method according to any one of claims 1 to 7, Computer readable medium.

20. In one or more non-transitory computer-readable media storing one or more instructions, The one or more instructions perform operations based on being executed by one or more processors, The above actions are, Comprising all steps of the method according to any one of claims 8 to 14, Computer readable medium.

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