Apparatus and method for performing entanglement distillation protocol in entanglement distillation protocol mode determined on basis of yield of entanglement distillation protocol in quantum communication system

Abstract

According to various embodiments of the present disclosure, a method performed by a first node is provided, the method comprising the steps of: transmitting, to a second node, information related to initial fidelity and target fidelity of an Einstein-Podolsky-Rosen (EPR) pair, the number of rounds in which an entanglement distillation protocol (EDP) is performed, and an EDP type; transmitting, to the second node, information related to the entanglement state of the EPR pair; determining an optimal EDP mode for optimal EPR yield; transmitting, to the second node, information about the optimal EDP mode for the optimal EPR yield; and performing the EDP on the basis of the optimal EDP mode.

Description

Apparatus and method for performing an entanglement distillation protocol in an entanglement distillation protocol mode determined based on the yield of the entanglement distillation protocol in a quantum communication system

[0001] The present disclosure relates to an apparatus and method for performing entanglement distillation protocol (EDP) in an entanglement distillation protocol mode (EDP mode) determined based on the yield of the entanglement distillation protocol (EDP) in a quantum communication system.

[0002]

[0003] Quantum entanglement distillation protocol is a technique that utilizes multiple entangled states with low fidelity and Local Operator and Classical Communication (LOCC) to share a few entangled states with high fidelity among multiple parties.

[0004] Various protocols have been developed for entanglement distillation protocols, including recurrence, QPA, breeding, hashing, and EDP protocols that utilize quantum error correction codes in a two-way manner. Among these, the recurrence and QPA protocols use classical communication bidirectionally from Alice to Bob and from Bob to Alice, while the breeding and hashing protocols use classical communication unidirectionally from Alice to Bob. Generally, two-way protocols have the advantage of being able to operate even in poor channel environments compared to one-way protocols, while one-way protocols have the advantage of utilizing fewer resources compared to two-way protocols.

[0005] High-fidelity EPR states or entangled states generated by the entanglement distillation protocol can be utilized for quantum teleportation or direct quantum communication, quantum key distribution, distributed quantum computing, etc.

[0006] In the case of recurrent mode, a significant increase in fidelity can generally be observed with each round. However, there are instances where the difference in output fidelity increases with the number of rounds, and fidelity is achieved that is excessively high compared to the target fidelity. Therefore, a protocol is required that utilizes minimal entanglement states while appropriately achieving the target fidelity.

[0007] In the case of (Nested Entanglement) pumping mode, there is an advantage of using less memory compared to recurrent mode, but it exhibits significantly degraded output fidelity performance when using the same entanglement state. Therefore, a method is needed to improve output fidelity by proposing a method that utilizes the QPA output entanglement state rather than the (Nested Entanglement) pumping mode.

[0008]

[0009] To solve the aforementioned problems, the present disclosure provides an apparatus and method for performing EDP in an entanglement distillation protocol mode (EDP mode) determined based on the yield of the entanglement distillation protocol (EDP) in a quantum communication system.

[0010] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs from the description below.

[0011]

[0012] According to various embodiments of the present disclosure, a method performed by a first node comprises the steps of: transmitting to a second node information related to the initial fidelity, target fidelity, number of rounds in which an entanglement distillation protocol (EDP) is performed, and an EDP type of an EPR pair; transmitting to the second node information related to the entanglement state of the EPR pair; determining an optimal EDP mode for an optimal EPR yield; transmitting to the second node information of the optimal EDP mode for the optimal EPR yield; and performing the EDP based on the optimal EDP mode.

[0013] According to various embodiments of the present disclosure, a method performed by a second node comprises: receiving from a first node information regarding the initial fidelity of an EPR pair (Einstein-Podolsky-Rosen pair), target fidelity, the number of rounds in which an EDP (entanglement distillation protocol) is performed, and an EDP type; receiving from the second node information regarding the entanglement state of the EPR pair; receiving from the first node information regarding an optimal EDP mode for an optimal EPR yield; and performing the EDP based on the optimal EDP mode.

[0014] According to various embodiments of the present disclosure, a first node is provided, comprising a transceiver, at least one processor, and at least one memory operably connected to at least one processor and storing instructions for performing operations when executed by at least one processor, wherein the operations include all steps of a method performed by the first node according to various embodiments of the present disclosure.

[0015] According to various embodiments of the present disclosure, a second node is provided, comprising a transceiver, at least one processor, and at least one memory operably connected to at least one processor and storing instructions for performing operations when executed by at least one processor, wherein the operations include all steps of a method performed by the second node according to various embodiments of the present disclosure.

[0016] According to various embodiments of the present disclosure, a control device for controlling a first node comprises at least one processor and at least one memory operably connected to at least one processor, wherein the at least one memory stores instructions for performing operations based on execution by at least one processor, and the operations include all steps of a method performed by the first node according to various embodiments of the present disclosure.

[0017] According to various embodiments of the present disclosure, a control device for controlling a second node comprises at least one processor and at least one memory operably connected to at least one processor, wherein the at least one memory stores instructions for performing operations based on execution by at least one processor, and the operations include all steps of a method performed by the second node according to various embodiments of the present disclosure.

[0018] 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 perform operations based on execution by one or more processors, and the operations are provided with all steps of a method performed by a first node according to various embodiments of the present disclosure.

[0019] 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 perform operations based on execution by one or more processors, and said operations are all steps of a method performed by a second node according to various embodiments of the present disclosure are provided.

[0020]

[0021] To solve the aforementioned problems, the present disclosure may provide an apparatus and method for performing EDP in an entanglement distillation protocol mode (EDP mode) determined based on the yield of the entanglement distillation protocol (EDP) in a quantum communication system.

[0022]

[0023] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. 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 one another to form new embodiments. Reference numerals in each drawing may denote structural elements.

[0024] Figure 1 is a diagram illustrating physical channels used in 3GPP systems and an example of typical signal transmission.

[0025] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0026] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0027] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0028] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0029] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0030] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0031] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0032] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0033] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0034] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0035] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0036] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0037] Figure 14 is a diagram illustrating an example of a THz communication application.

[0038] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0039] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0040] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0041] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0042] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0043] 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.

[0044] FIG. 21 is a drawing illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0045] 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.

[0046] FIG. 23 is a diagram illustrating an example of the execution process of the QPA protocol in a system applicable to the present disclosure.

[0047] FIG. 24 is a diagram illustrating an example of the process of the Double selection protocol in a system applicable to the present disclosure.

[0048] FIG. 25 is a diagram illustrating an example of a QECCs-based hashing protocol in a system applicable to the present disclosure.

[0049] FIG. 26 is a drawing 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. 27 is a diagram illustrating an example of the process of a transmitter and a receiver measuring qubits in a system applicable to the present disclosure.

[0051] FIG. 28 is a diagram illustrating an example of the structure of a unidirectional EDP technique in a system applicable to the present disclosure.

[0052] FIG. 29 is a drawing illustrating an example of a 4-round recurrent mode in a system applicable to the present disclosure.

[0053] FIG. 30 is a drawing illustrating an example of a (Nested) Entanglement pumping mode in a system applicable to the present disclosure.

[0054] FIG. 31 is a diagram illustrating an example of an EDP execution circuit after a transformation process of a Bell diagonal state utilizing various unitary operations in a system applicable to the present disclosure.

[0055] FIG. 32 is a diagram illustrating an example of a Recurrence protocol (circuit performing EDP after changing the Werner state by twirling) in a system applicable to the present disclosure.

[0056] FIG. 33 is a diagram illustrating an example of the process of executing the 2-1 EDP protocol through operator adaptation in a system applicable to the present disclosure.

[0057] FIG. 34 is a diagram illustrating an example of a 4 Round Recurrent protocol and an EDP block number in a system applicable to the present disclosure.

[0058] FIG. 35 is a diagram illustrating an example of a representation of an EDP protocol in which Blocks 3, 7, and 11 are removed in a system applicable to the present disclosure.

[0059] FIG. 36 is a diagram illustrating an example of the connection relationship of EDP blocks in which Blocks 3, 7, and 11 are removed in a system applicable to the present disclosure.

[0060] FIG. 37 is a diagram illustrating an example of a communication protocol that determines the EDP mode based on Yield in a system applicable to the present disclosure.

[0061] FIG. 38 is a drawing illustrating an example of a Yield Optimal EDP in a system applicable to the present disclosure.

[0062] FIG. 39 is a diagram illustrating an example of an Entanglement Distillation Protocol (or Entanglement Purification Protocol, EPP) in a system applicable to the present disclosure.

[0063] FIG. 40 is a drawing illustrating an example of bipartite entanglement purification with single selection in a system applicable to the present disclosure.

[0064] FIG. 41 is a diagram illustrating an example of an EDP in recurrent mode in a system applicable to the present disclosure.

[0065] FIG. 42 is a drawing illustrating an example of an EDP in pumping mode in a system applicable to the present disclosure.

[0066] FIG. 43 is a diagram illustrating an example of a method in which some EDP of the Recurrent mode is not performed in a system applicable to the present disclosure.

[0067] FIG. 44 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0068] FIG. 45 is a diagram illustrating an example of location search using a reuse qubit candidate searching algorithm for a block removed in a system applicable to the present disclosure.

[0069] FIG. 46 is a diagram illustrating an example of performance when an EDP block is removed in a system applicable to the present disclosure.

[0070] FIG. 47 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0071] FIG. 48 is a diagram illustrating an example of performance when an EDP block is removed in a system applicable to the present disclosure.

[0072] FIG. 49 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0073] FIG. 50 is a diagram illustrating an example of communication signaling in a technique for removing EDP blocks to optimize yield in a system applicable to the present disclosure.

[0074] FIG. 51 is a diagram illustrating an example of an EDP protocol in which a Block is removed in a system applicable to the present disclosure.

[0075] FIG. 52 is a diagram illustrating an example of the connection relationship of EDP blocks in a system applicable to the present disclosure.

[0076] FIG. 53 is a diagram illustrating an example of a communication protocol that determines the EDP mode based on Yield in a system applicable to the present disclosure.

[0077] FIG. 54 is a diagram illustrating an example of the operation process of a first node in a system applicable to the present disclosure.

[0078] FIG. 55 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0079] FIG. 56 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0080] FIG. 57 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0081] FIG. 58 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0082] FIG. 59 illustrates a signal processing circuit for a transmission signal.

[0083] FIG. 60 shows another example of a wireless device applicable to various embodiments of the present disclosure.

[0084] FIG. 61 illustrates a portable device applicable to various embodiments of the present disclosure.

[0085] FIG. 62 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0086] FIG. 63 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0087] FIG. 64 illustrates an XR device applied to various embodiments of the present disclosure.

[0088] FIG. 65 illustrates a robot applicable to various embodiments of the present disclosure.

[0089] FIG. 66 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." Alternatively, 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 used 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." Additionally, 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 synonymous with "at least one of A and B."

[0094] Additionally, in various embodiments of the present disclosure, “at least one of A, B and C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Also, “at least one of A, B or C” or “at least one of A, B and / or C” may 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, the "control information" of 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 described individually within one 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), and 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 part of the UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is part of E-UMTS (Evolved UMTS) using 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 of explanation, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical scope of this disclosure is not limited thereto. LTE refers to technology from 3GPP TS 36.xxx Release 8 onwards. Specifically, LTE technology from 3GPP TS 36.xxx Release 10 onwards is referred to as LTE-A, and LTE technology from 3GPP TS 36.xxx Release 13 onwards is referred to as LTE-A pro. 3GPP NR refers to technology from TS 38.xxx Release 15 onwards. 3GPP 6G may refer to technology from TS Release 17 and / or Release 18 onwards. "xxx" indicates a specific standard document number. LTE / NR / 6G may be collectively referred to as 3GPP systems. Regarding background technology, terms, abbreviations, etc. used in the description of this disclosure, reference may be made to matters described in standard documents published prior to this disclosure. For example, the following documents may be referenced.

[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 physical channels used in 3GPP systems and an example of typical signal transmission.

[0119] In a wireless communication system, a terminal receives information from a base station via a downlink (DL) and transmits information to the base station via an uplink (UL). The information transmitted and received by 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 they transmit and receive.

[0120]

[0121] When the 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 do this, 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. After that, the terminal receives a Physical Broadcast Channel (PBCH) from the base station to obtain broadcast information within the cell. Meanwhile, during the initial cell search phase, the terminal receives a Downlink Reference Signal (DL RS) 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 Control Channel (PDSCH) according to the information carried on the PDCCH (S12).

[0124]

[0125] Meanwhile, when connecting to a base station for the first time or when there are no wireless resources available for signal transmission, the terminal may perform a Random Access Procedure (RACH) with respect to the base station (S13 to S16). To this end, the terminal transmits a specific sequence as a preamble through a Physical Random Access Channel (PRACH) (S13 and S15), and may receive a response message (RAR (Random Access Response) message) for the preamble through 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] A terminal that has performed the procedure described above may subsequently perform PDCCH / PDSCH reception (S17) and Physical Uplink Shared Channel (PUSCH) / Physical Uplink Control Channel (PUCCH) transmission (S18) as a general uplink / downlink signal transmission procedure. In particular, the terminal may receive Downlink Control Information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and the format may be applied differently depending on the purpose of use.

[0128]

[0129] Meanwhile, control information transmitted by the terminal to the base station via the uplink or received by the terminal 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 control information such as the above-mentioned 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 relevant signal to the terminal through the downlink channel described below, and the terminal receives the relevant signal from the base station through the downlink channel described below.

[0134]

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

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

[0137]

[0138] (2) Physical Downlink Control Channel (PDCCH)

[0139] A PDCCH carries downlink control information (DCI) and applies methods such as QPSK modulation. A single PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) depending on the Aggregation Level (AL). A single CCE consists of 6 Resource Element Groups (REGs). A single REG is defined by one OFDM symbol and one (P)RB.

[0140] The terminal obtains the DCI transmitted over the PDCCH by performing decoding (also known as blind decoding) on ​​a set of PDCCH candidates. The set of PDCCH candidates decoded by the terminal is defined as the PDCCH Search Space set. The Search Space set may be a common search space or a UE-specific search space. The terminal may obtain the DCI by monitoring PDCCH candidates within one or more Search Space sets configured by the MIB or upper-layer signaling.

[0141]

[0142] Uplink Channel Structure

[0143] The terminal transmits a relevant signal to the base station through the uplink channel described below, and the base station receives the relevant 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, DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform, etc. When PUSCH is transmitted based on a DFT-s-OFDM waveform, the terminal applies transform precoding to transmit PUSCH. For example, if transform precoding is not possible (e.g., transform precoding is disabled), the terminal transmits PUSCH based on a CP-OFDM waveform, and if transform precoding is enabled (e.g., transform precoding is enabled), the terminal can transmit PUSCH based on a CP-OFDM waveform or a DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants within DCI or semi-statically scheduled based on upper layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)) configured grants. PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0146] (2) Physical uplink control channel (PUCCH)

[0147] A 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] The following describes new radio access technology (new RAT, NR).

[0150] As more communication devices require larger communication capacities, the need for enhanced mobile broadband communication compared to existing radio access technology (RAT) is emerging. Furthermore, Massive Machine Type Communications (MTC), which connects multiple devices and objects to provide various services anytime and anywhere, is also one of the major issues to be considered in next-generation communication. In addition, communication system designs that consider services / terminals sensitive to reliability and latency are being discussed. Thus, the introduction of next-generation radio access technology considering enhanced mobile broadband communication, massive MTC, and Ultra-Reliable and Low Latency Communication (URLC) is being discussed, and for convenience in the various embodiments of this disclosure, such technology is 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 gNBs and / or eNBs that provide user plane and control plane protocol termination to terminals. FIG. 1 illustrates a case where only gNBs are included. The gNBs and eNBs are connected to each other via Xn interfaces. The gNBs and eNBs are connected to the 5G Core Network (5GC) via NG interfaces. More specifically, they are connected to the access and mobility management function (AMF) via NG-C interfaces and to the user plane function (UPF) via NG-U interfaces.

[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 RRM, RB control, Connection Mobility Control, Radio Admission Control, Measurement Configuration & 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 may be applied to other 5G usage scenarios not illustrated in FIG. 4.

[0160] Referring to FIG. 4, the three major requirement areas of 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 multiple areas for optimization, while others may focus on only one key performance indicator (KPI). 5G supports these various use cases in a flexible and reliable manner.

[0161] eMBB focuses on overall improvements in data speed, latency, user density, and the capacity and coverage of mobile broadband access. eMBB aims for a throughput of approximately 10 Gbps. eMBB far surpasses basic mobile internet access and covers media and entertainment applications ranging from rich interactive tasks to cloud or augmented reality. Data is one of the core drivers of 5G, and dedicated voice services may not be seen for the first time in the 5G era. In 5G, voice is expected to be processed simply as an application using the data connection provided by the communication system. The main causes of the increased traffic volume are the growing size of content and the increase in the number of applications requiring high data transfer rates. Streaming services (audio and video), interactive video, and mobile internet connectivity will become more widely used 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 growing rapidly on mobile communication platforms, applicable to both business and entertainment. Cloud storage is a specific use case driving the growth of uplink data transfer rates. 5G is also used for remote work in the cloud, requiring much lower end-to-end latency to maintain an excellent user experience when haptic interfaces are used. In entertainment, for example, cloud gaming and video streaming are another key factor increasing the demand for mobile broadband capabilities. Entertainment is essential on smartphones and tablets anywhere, including in highly mobile environments such as trains, cars, and airplanes. Other use cases include augmented reality for entertainment and information retrieval. Here, augmented reality requires very low latency and instantaneous data volumes.

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

[0163] URLLC is ideal for automotive communications, industrial control, factory automation, remote operation, smart grids, and public safety applications by enabling devices and machines to communicate with high reliability, very low latency, and high availability. URLLC aims for a latency of approximately 1ms. URLLC encompasses new services that will transform industries through ultra-reliable / low-latency links, such as remote control of critical infrastructure and autonomous vehicles. Levels of reliability and latency are essential for smart grid control, industrial automation, robotics, and drone control and coordination.

[0164] Next, we will examine in more detail the multiple usage examples included within the triangle of Fig. 4.

[0165] 5G can complement Fiber-to-the-Home (FTTH) and cable-based broadband (or Docsis) as a means of providing streams rated at hundreds of megabits per second to gigabits per second. These high speeds may be required for virtual reality (VR) and augmented reality (AR), as well as for delivering TV at resolutions of 4K or higher (6K, 8K, and above). VR and AR applications include near-immersive sports matches. Certain applications may require special network configurations. For example, in the case of VR games, game companies may need to integrate core servers with the network operator's edge network servers to minimize latency.

[0166] The automotive sector is expected to become a significant new driving force for 5G, with numerous use cases for mobile communications within vehicles. For example, passenger entertainment requires both high capacity and high mobile broadband simultaneously. This is because future users will continue to expect high-quality connectivity regardless of their location or speed. Another use case in the automotive sector is the augmented reality dashboard. Through an augmented reality contrast board, drivers can identify objects in the dark overlaid on what they are seeing through the windshield. The augmented reality dashboard overlays information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable communication between vehicles, information exchange between vehicles and supporting infrastructure, and information exchange between vehicles and other connected devices (e.g., devices accompanying pedestrians). Safety systems will allow drivers to drive more safely by guiding them to alternative courses of action, thereby reducing the risk of accidents. The next step will be remotely controlled vehicles or autonomous vehicles. This requires highly reliable and very fast communication between different autonomous vehicles and / or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving activities, allowing drivers to focus only on traffic anomalies that the vehicle itself cannot identify. The technical requirements for autonomous vehicles demand ultra-low latency and ultra-high reliability to increase traffic safety to a level that is unattainable by humans.

[0167] Smart cities and smart homes, referred to as a smart society, will be embedded with high-density wireless sensor networks. Distributed networks of intelligent sensors will identify conditions for maintaining the cost-effective and energy-efficient maintenance of the city or home. A similar setup can be implemented for each household. Temperature sensors, window and heating controllers, burglar alarms, and home appliances are all wirelessly connected. Many of these sensors typically require low data transmission rates, low power consumption, and low cost. However, for example, real-time HD video may be required by certain types of devices for surveillance.

[0168] The consumption and distribution of energy, including heat or gas, are becoming highly decentralized, requiring automated control of distributed sensor networks. Smart grids interconnect these sensors using digital information and communication technologies to collect information and act accordingly. Since this information may include the behavior of suppliers and consumers, smart grids can improve efficiency, reliability, economic viability, production sustainability, and the automated distribution of fuels such as electricity. Smart grids can also be viewed as other sensor networks with low latency.

[0169] The health sector possesses numerous applications that can benefit from mobile communications. Communication systems can support telemedicine, providing clinical care from remote locations. This helps reduce distance barriers and improves access to medical services that are not consistently available in remote rural areas. It is also used to save lives during critical medical care and emergencies. Mobile communication-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 involves high installation and maintenance costs. 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 latency, reliability, and capacity comparable to cables, while also simplifying their management. Low latency and a very low probability of error are new requirements that 5G needs to meet.

[0171] Logistics and cargo tracking are important use cases for mobile communications that use location-based information systems to enable the tracking of inventory and packages anywhere. Use cases for logistics and cargo tracking typically require low data rates but may require wide coverage and reliable location information.

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

[0173]

[0174] 6G System General

[0175] The 6G (wireless communication) system aims for (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be seen in four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. In other words, Table 1 is a table representing an example of the requirements for 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 higher simultaneous wireless connectivity than 5G wireless communication systems. URLLC, a key feature of 5G, will become an even more dominant technology in 6G communication by providing end-to-end latency of less than 1ms. Unlike the frequently used area spectrum efficiency, 6G systems will exhibit significantly superior volume spectrum efficiency. 6G systems can provide very long battery life and advanced battery technologies for energy harvesting, meaning mobile devices in 6G systems will not require separate charging. New network characteristics in 6G may include the following.

[0183] - Satellite Integrated Network: 6G is expected to be integrated with satellites to provide a global mobile population. Integrating terrestrial, satellite, and airborne networks into a single wireless communication system is crucial for 6G.

[0184] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing 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: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0187] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0188] - Small cell networks: The idea of ​​small cell networks was introduced to improve the quality of received signals in cellular systems as a result of increased throughput, energy efficiency, and spectrum efficiency. Consequently, small cell networks are an essential feature of communication systems for 5G and beyond 5G (5GB). Therefore, 6G communication systems also adopt the characteristics of small cell networks.

[0189] - Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

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

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

[0192] - Softwarization and virtualization: Softwarization and virtualization are two important features that form the basis of the design process in 5GB networks to ensure flexibility, reconfigurability, and programmability. Additionally, billions of devices can be shared across a shared physical infrastructure.

[0193]

[0194] Core implementation technology of 6G systems

[0195] Artificial Intelligence

[0196] The most critical and newly introduced technology for 6G systems is AI. AI was not involved in 4G systems. 5G systems will support AI partially or to a very limited extent. However, 6G systems will be supported by AI for complete automation. Advancements in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency.

[0197] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). 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.

[0198] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application layer and network layer, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward 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 signal processing and communication mechanisms based on AI drivers rather than traditional communication frameworks in terms of fundamental signal processing and communication mechanisms. Examples 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.

[0199] Machine learning can be used for channel estimation and channel tracking, and for power allocation and interference cancellation in the physical layer of the downlink (DL). In addition, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0200] However, the application of DNNs for transmission at the physical layer may have the following problems.

[0201] Deep learning-based AI algorithms require a vast amount of training data to optimize training parameters. However, due to limitations in acquiring training data from specific channel environments, a large amount of offline training data is used. Consequently, static training on training data in specific channel environments can lead to contradictions between the dynamic characteristics and diversity of wireless channels.

[0202] Furthermore, current deep learning primarily targets real signals. However, signals at the physical layer of wireless communication are complex signals. Further research is needed on neural networks that detect complex domain signals to match the characteristics of wireless communication signals.

[0203] Below, we will take a closer look at machine learning.

[0204] Machine learning refers to a series of operations for training machines to create machines capable of performing tasks that humans can or find difficult to do. Machine learning requires data and learning models. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0205] The purpose of neural network training is to minimize output errors. It is a process that repeatedly inputs training data into a neural network, calculates the error between the network's output and the target for the training data, and updates the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error.

[0206] Supervised learning uses training data with correct answers labeled, whereas unsupervised learning may not have correct answers labeled. That is, for example, in the case of supervised learning regarding data classification, the training data may consist of data where each training data point is labeled with a category. Labeled training data is input into a neural network, and an error can be calculated by comparing the network's output (category) with the labels of the training data. The calculated error is backpropagated within the neural network (i.e., from the output layer to the input layer), and through backpropagation, the connection weights of each node in each layer of the neural network can be updated. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculations on 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, efficiency can be increased by using a high learning rate in the early stages of neural network training to enable the network to quickly achieve a certain level of performance, and accuracy can be increased by using a low learning rate in the later stages of training.

[0207] The learning method may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from the transmitting end at the receiving end in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0208] Learning models correspond to the human brain, and while the most basic linear models can be considered, a machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0209] The neural network cores used for learning methods are broadly classified into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN).

[0210] An artificial neural network is an example of connecting multiple perceptrons.

[0211]

[0212] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0213] Referring to Fig. 6, the entire process of inputting an input vector x=(x1,x2,...,xd), multiplying each component by a weight (W1,W2,...,Wd), summing all the results, and then applying an activation function σ(·) is called a perceptron. A large artificial neural network structure can also apply input vectors to different multi-dimensional perceptrons by extending the simplified perceptron structure illustrated in Fig. 6. For convenience of explanation, input or output values ​​are referred to as nodes.

[0214] Meanwhile, the perceptron structure illustrated in Fig. 6 can be described as consisting of a total of three layers based on input and output values. An artificial neural network can be represented as shown in Fig. 7, in which there are H (d+1) dimensional perceptrons between the 1st layer and the 2nd layer, and K (H+1) dimensional perceptrons between the 2nd layer and the 3rd layer.

[0215]

[0216] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0217] 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 and output layers are called hidden layers. Although the example in Fig. 7 shows three layers, the input layer is excluded when counting the actual number of layers in an artificial neural network, so it can be viewed as having a total of two layers. An artificial neural network is constructed by connecting perceptrons of basic blocks in a two-dimensional manner.

[0218] The aforementioned input layer, hidden layer, and output layer can be applied not only to multilayer perceptrons but also to various artificial neural network structures such as CNNs and RNNs, which will be described later. As the number of hidden layers increases, the artificial neural network becomes deeper, and the machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called Deep Learning. In addition, the artificial neural network used for Deep Learning is called a Deep Neural Network (DNN).

[0219]

[0220] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0221] The deep neural network illustrated in Fig. 8 is a multilayer perceptron composed of eight hidden layers plus eight output layers. The structure of the multilayer perceptron is referred to as a fully-connected neural network. In a fully-connected neural network, there are no connections between nodes located in the same layer, and connections exist only between nodes located in adjacent layers. A DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, which can be usefully applied to identify correlation characteristics between inputs and outputs. Here, correlation characteristics may refer to the joint probability of the input and output.

[0222] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0223]

[0224] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0225] In a DNN, nodes located within a single layer are arranged in a one-dimensional vertical direction. However, Figure 9 assumes a case where nodes are arranged two-dimensionally, with w nodes horizontally and h nodes vertically (the convolutional neural network structure of Figure 9). In this case, since a weight is applied for each connection during the connection process from a single input node to a hidden layer, a total of hYw weights must be considered. Since there are hYw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0226] The convolutional neural network of Fig. 9 has a problem in which the number of weights increases exponentially with the number of connections. Therefore, instead of considering all mode connections between adjacent layers, it is assumed that there are small filters, and weighted sum and activation function operations are performed on the parts where filters overlap, as shown in Fig. 10.

[0227]

[0228] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0229] A single filter has weights corresponding to its size, and the weights can be trained to extract and output specific features on an image as factors. In Fig. 10, a filter of size 3Y3 is applied to the top-left 3Y3 region of the input layer, and the output value resulting from the weighted sum and activation function operation for the corresponding node is stored in z22.

[0230] The above filter performs weighted sum and activation function operations while scanning the input layer and moving by a fixed interval horizontally and vertically, and places the output value at the current filter position. This method of operation is similar to the convolution operation on 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 having multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0231] In the convolution layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located within the area covered by the filter, starting from the node where the current filter is located. As a result, a single filter can be utilized to focus on features of a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a 2D area serves as an important judgment criterion. Meanwhile, multiple filters can be applied immediately before the convolution layer in a CNN, and multiple output results can be generated through the convolution operation of each filter.

[0232] Meanwhile, depending on the data attributes, there may be data where sequence characteristics are important. A structure that applies a method to an artificial neural network in which elements of the data sequence are input one by one at each timestep, taking into account the length variability and sequence relationships of such sequence data, and the output vector (hidden vector) of the hidden layer output at a specific timestep is input along with the next element in the sequence is called a recurrent neural network structure.

[0233]

[0234] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0235] Referring to Fig. 11, the recurrent neural network (RNN) is structured such that, in the process of inputting elements (x1(t), x2(t), ..., xd(t)) of a time point t in a data sequence into a fully connected neural network, the previous time point t-1 is input along with the hidden vector (z1(t-1), z2(t-1), ..., zH(t-1)), and a weighted sum and activation function are applied. The reason for passing the hidden vector to the next time point in this manner is that the information in the input vectors from previous time points is considered to be accumulated in the hidden vector of the current time point.

[0236]

[0237] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0238] Referring to Fig. 12, the recurrent neural network operates on the input data sequence in a predetermined time sequence.

[0239] When the input vector (x1(t), x2(t), ..., xd(t)) at time point 1 is input into 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 activation function. This process is performed repeatedly up to time point 2, time point 3, ..., time point T.

[0240] 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 usefully applied to sequence data (e.g., natural language processing).

[0241] In addition to DNN, CNN, and RNN, it includes various deep learning techniques such as Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), and Deep Q-Network as neural network cores used for learning, and can be applied in fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0242] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application layer and network layer, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward 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 signal processing and communication mechanisms based on AI drivers rather than traditional communication frameworks in terms of fundamental signal processing and communication mechanisms. Examples 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.

[0243] THz (Terahertz) communication

[0244] Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz–300 GHz band range (Sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the Sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz–3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz–3 THz band is part of the broadband, it lies at the boundary of the broadband and immediately following the RF band. Therefore, this 300 GHz–3 THz band exhibits similarities to RF.

[0245]

[0246] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0247] Key characteristics of THz communication include (i) widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array technologies that can overcome range limitations.

[0248] Optical wireless technology

[0249] OWC technology has been planned for 6G communication in addition to RF-based communication for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul network connections. Although OWC technology has already been in use since 4G communication systems, 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 broadband-based FSO communication are already well-known technologies. Communication based on optical radio technology can provide very high data rates, low latency, and secure communication. LiDAR can also be utilized for ultra-high resolution 4D mapping in 6G communication based on broadband.

[0250] FSO Backhaul Network

[0251] 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. Consequently, FSO can be a good technology for providing backhaul connectivity in 6G systems in conjunction with fiber optic networks. Using FSO enables very long-distance communication over distances of more than 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 backhaul connectivity.

[0252] Massive MIMO technology

[0253] One of the key technologies for improving spectrum efficiency is the application of MIMO technology. As MIMO technology improves, spectrum efficiency also improves. Therefore, large-scale MIMO technology will be important in 6G systems. Since MIMO technology utilizes multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must also be given important consideration to enable data signals to be transmitted through one or more paths.

[0254] blockchain

[0255] Blockchain will become a critical technology for managing massive amounts of data in future communication systems. As 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. Blockchain is managed via a peer-to-peer (P2P) network and can exist without being managed by a centralized authority or server. Data in a blockchain is collected together and organized into blocks. These blocks are linked together and protected using encryption. Blockchain inherently complements large-scale IoT perfectly through enhanced interoperability, security, privacy, stability, and scalability. Therefore, blockchain technology provides various capabilities such as inter-device interoperability, large-scale data traceability, autonomous interaction with other IoT systems, and the large-scale connectivity stability of 6G communication systems.

[0256] 3D Networking

[0257] 6G systems integrate terrestrial and air networks to support vertically scalable user communications. 3D BS will be provided via low-orbit satellites and UAVs. By adding new dimensions in terms of altitude and associated degrees of freedom, 3D connectivity differs significantly from existing 2D networks.

[0258] Quantum communication

[0259] Unsupervised reinforcement learning of networks is promising in the context of 6G networks. Supervised learning methods cannot label the vast amount of data generated in 6G. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously construct representations of complex networks. Combining reinforcement learning and unsupervised learning enables the operation of networks in a truly autonomous manner.

[0260] unmanned aerial vehicles

[0261] Unmanned Aerial Vehicles (UAVs) or drones will become a critical element in 6G wireless communication. In most cases, high-speed data wireless connectivity is provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs possess specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled degrees of freedom for mobility. During emergencies, such as natural disasters, the deployment of ground communication infrastructure is not economically feasible, and sometimes services cannot be provided in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in the field of wireless communication. This technology facilitates the three fundamental requirements of wireless networks: 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 critical technologies for 6G communication.

[0262] Cell-free Communication

[0263] The tight integration of multiple frequencies and heterogeneous communication technologies is critical to 6G systems. Consequently, users can seamlessly move from one network to another without the need for any manual configuration on their devices. The best network among available communication technologies is automatically selected. This will break the limitations of the cellular concept in wireless communication. Currently, user movement from one cell to another in high-density networks causes excessive handovers, leading to handover failures, delays, data loss, and the "ping-pong" effect. 6G cell-free communication will overcome all of these issues and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as different heterogeneous radios on devices.

[0264] Wireless Information and Energy Transmission Integration

[0265] WIET uses the same fields and waves as wireless communication systems. In particular, sensors and smartphones will be charged using wireless power transmission during communication. WIET is a promising technology for extending the lifespan of wireless battery charging systems. Therefore, devices without batteries will be supported in 6G communication.

[0266] Integration of Sensing and Communication

[0267] Autonomous wireless networks are capable of continuously detecting dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communication to support autonomous systems.

[0268] Integration of access backhaul networks

[0269] In 6G, the density of access networks will be enormous. Each access network will be connected via backhaul connections such as fiber optics and FSO networks. To cope with a very large number of access networks, there will be tight integration between access and backhaul networks.

[0270] Holographic beam forming

[0271] Beamforming is a signal processing procedure that adjusts an antenna array to transmit wireless signals in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, such as a high call-to-noise ratio, interference prevention and rejection, and high network efficiency. Holographic Beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it utilizes software-defined antennas. HBF will be a highly effective approach for the efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

[0272] Big data analysis

[0273] Big data analysis is a complex process for analyzing various large-scale data sets or big data. This process ensures perfect data management by uncovering information such as hidden data, unknown correlations, and customer preferences. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process vast amounts of data in 6G systems.

[0274] Large Intelligent Surface (LIS)

[0275] THz band signals exhibit strong directivity, which can lead to numerous dead zones caused by obstacles. Consequently, LIS technology becomes important as it allows for the expansion of communication coverage, enhanced communication stability, and the provision of additional value-added services by installing LIS near these dead zones. An LIS is an artificial surface made of electromagnetic materials capable of altering 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 operational mechanism. Furthermore, LIS offers the advantage of low power consumption because it operates as a reconfigurable reflector with passive elements—that is, by passively reflecting signals without using an active RF chain. Additionally, since each passive reflector in an LIS must independently adjust the phase shift of the incident signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift through the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0276]

[0277] Terahertz (THz) wireless communication general

[0278]

[0279] THz wireless communication utilizes THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 10¹² Hz) for wireless communication, and can refer to terahertz (THz) band wireless communication using very high carrier frequencies of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) they penetrate non-metallic / non-polar materials well compared to visible light / infrared light, and because their wavelengths are shorter than RF / millimeter waves, they have high directivity and can be beam focused. 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–170 GHz) or H-band (220 GHz–325 GHz) bands, which have low propagation loss due to molecular absorption in the air. Standardization discussions regarding THz wireless communication are being conducted primarily by the IEEE 802.15 THz working group in addition to 3GPP, and standard documents published by the IEEE 802.15 Task Group (TG3d, TG3e) may elaborate on or supplement the contents described in the various embodiments of this disclosure. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0280]

[0281] Figure 14 is a diagram illustrating an example of a THz communication application.

[0282] As illustrated in FIG. 14, THz wireless communication scenarios can be classified into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle connections 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 and wireless connections in data centers, and near-field communication, such as kiosk downloading.

[0283] Table 2 below shows an example of a technology that can be used in THz waves.

[0284] 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

[0285]

[0286] THz wireless communication can be classified based on the methods for generating and receiving THz. THz generation methods can be classified into optical or electronic device-based technologies.

[0287]

[0288] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0289] Methods for generating THz using electronic components include using semiconductor devices such as Resonant Tunneling Diodes (RTDs), using local oscillators and multipliers, using Monolithic Microwave Integrated Circuits (MMICs) based on compound semiconductor High Electron Mobility Transistors (HEMTs), and using Si-CMOS based integrated circuits. In the case of Fig. 15, a doubler, tripler, or multiplier is applied to increase the frequency, and the signal passes through a subharmonic mixer and is radiated by the antenna. Since the THz band forms high frequencies, a multiplier is essential. Here, the multiplier is a circuit that produces an output frequency N times that of the input, matches it to the desired harmonic frequency, and filters out all other frequencies. Additionally, beamforming may be implemented by applying an array antenna or similar device to the antenna in Fig. 15. In Fig. 15, IF represents the intermediate frequency, tripler and multipler represent multipliers, PA represents the power amplifier, LNA represents the low noise amplifier, and PLL represents the phase-locked loop.

[0290]

[0291] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0292] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0293] Optical device-based THz wireless communication technology refers to a method of generating and modulating THz signals using optical devices. Optical device-based THz signal generation technology is a technique that generates ultra-high-speed optical signals using lasers and optical modulators, and converts them into THz signals using ultra-high-speed photodetectors. Compared to technology that uses only electronic devices, this technology makes it easier to increase the frequency, enables the generation of high-power signals, and allows for flat response characteristics over a wide frequency band. To generate optical device-based THz signals, a laser diode, a broadband optical modulator, and an ultra-high-speed photodetector are required, as shown in Fig. 16. In the case of Fig. 16, light signals from 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 uses light waves to transmit electrical signals in order to provide coupling with electrical isolation between circuits or systems, and a Uni-Travelling Carrier Photo-Detector (UTC-PD) is a type of photodetector that uses electrons as active carriers and reduces the electron travel time through bandgap grading. The UTC-PD is capable of photodetect at 150 GHz or higher. In FIG. 17, an Erbium-Doped Fiber Amplifier (EDFA) represents an erbium-doped fiber amplifier, a Photo Detector (PD) represents a semiconductor device capable of converting optical signals into electrical signals, an Optical Sub Assembly (OSA) represents an optical module that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component, and a Digital Storage Oscilloscope (DSO) represents a digital storage oscilloscope.

[0294]

[0295] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0296] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0297] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0298] Generally, the phase of a signal can be changed by passing an optical source of a laser through an optical wave guide. At this time, data is carried by changing electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An O / E converter can generate THz pulses based on optical rectification by a nonlinear crystal, O / E conversion by a photoconductive antenna, and emission from a bundle of relativistic electrons. Terahertz pulses generated in the above manner can have lengths ranging from femtoseconds to picoseconds. The photoelectric converter (O / E converter) performs down-conversion by utilizing the non-linearity of the device.

[0299] When considering the usage of the terahertz spectrum, it is highly likely that multiple contiguous GHz bands will be used for fixed or mobile service applications for terahertz systems. According to outdoor scenario criteria, available bandwidth can be classified based on an 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 length of a terahertz pulse (THz pulse) for a single carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0300] Effective down-conversion from the infrared (IR) band to the terahertz (THz) band depends on how the nonlinearity of the photoelectric converter (O / E converter) is utilized. In other words, to achieve down-conversion to the desired terahertz band, it is required to design an O / E converter with the most ideal nonlinearity for transferring to that specific band. If an O / E converter that does not match the target frequency band is used, there is a high probability of errors occurring regarding the amplitude and phase of the corresponding pulse.

[0301] In a single-carrier system, a terahertz transceiver system can be implemented using a single photoelectric converter. Depending on the channel environment, in a multi-carrier system, as many photoelectric converters as there are carriers may be required. This phenomenon will be particularly pronounced in multi-carrier systems utilizing multiple broadbands according to the plans related to the aforementioned spectrum applications. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on a photoelectric converter can be transmitted in a specific resource region (e.g., a specific frame). The frequency domain of the specific resource region may include multiple chunks. Each chunk may consist of at least one component carrier (CC).

[0302]

[0303] Detailed description of various embodiments of the present disclosure

[0304] Various embodiments of the present disclosure will be described in more detail below.

[0305]

[0306] The present disclosure relates to a method and apparatus for performing an entanglement distillation protocol in an entanglement distillation protocol mode determined based on the yield of the entanglement distillation protocol in a quantum communication system.

[0307]

[0308] Background art for various embodiments of the present disclosure

[0309] Bell state and Bell basis

[0310] The Bell state is the simplest example of quantum entanglement and refers to the four quantum states formed by two qubits in a maximally entangled state, as shown in the following mathematical equation 1. This can be viewed as a maximally entangled basis in 4-dimensional Hilbert space for two qubits, and is called the Bell basis.

[0311]

[0312] Creation of Bell state

[0313] 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.

[0314] A Bell state can be generated through a two-qubit quantum circuit composed of a Hadamard gate and a CNOT gate (controlled not gate) as shown in Fig. 20. Four two-qubit inputs For this, it has a bell state output as shown in Table 3. Table 3 shows the input and output states of the bell state generation circuit.

[0315]

[0316]

[0317]

[0318] Bell state measurement / Bell state analysis

[0319] FIG. 21 is a drawing illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0320] As previously explained, since Bell states form a normal orthogonal basis, appropriate measurements can be defined to identify the four Bell states; this is referred to as Bell state measurement or Bell state analysis. Bell state measurement involves determining which of the four quantum entanglement states defined by Bell states the state of two qubits belongs to. If the order of the CNOT gate and 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. For the four quantum entanglement states corresponding to Bell states, measurement results as shown in Table 4 can be obtained. Table 4 shows the input and output states of the Bell state measurement circuit.

[0321]

[0322]

[0323] Entanglement

[0324] Entanglement is a property that plays a very important role in distinguishing quantum systems from classical information. Entanglement refers to a state in which the results of different observations are closely related to each other. The entangled state in a quantum system acts more strongly than any correlation existing in classical mechanics. Two qubits can be represented in Hilbert space as a superposition of four fundamental quantum states. Here, the aforementioned four fundamental quantum states are It includes. The fundamental quantum states of two qubits can be represented through tensor operations on the fundamental states of individual qubits; however, if the states of two qubits cannot be represented by the tensor product of a single qubit, such qubit states are called entangled states. As representative examples of entangled qubits, there are four cases referred to as EPR (Einstein-Podolsky-Rosen) states, which are as shown in Equation 2 below.

[0325]

[0326] The above EPR state is also called the Bell state, and in each qubit, the measurement result of the qubit located ahead always affects the measurement of the qubit located behind. In addition, the above four Pure States are all Maximally Entangled States and constitute the vertical basis of the 2-qubit Hilbert Space.

[0327]

[0328] Continuous Quantum Error

[0329] In conventional information systems, information consists of '0' and '1', and errors are represented when '0' changes to '1' or '1' changes to '0'. Qubit It can be thought of as a single point existing on the surface of a Bloch sphere; when an error occurs in a qubit in a conventional information system, it is called a bit flip error. Such an error means that the value of 'a' changes to the value of 'b', which implies that when measuring a qubit, the measurement probability has changed from the initial value due to the error. Other forms of errors different from those in conventional information systems include class There is a phase flip error in which the phase between changes by 180 degrees. Errors can occur with arbitrary phases along the X-axis or Z-axis, and thus quantum errors can have continuous phases.

[0330]

[0331] Quantum Error Correcting Codes (QECCs)

[0332] 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.

[0333] Similar to classical communication, the quality of transmitted information in quantum communication processes can be affected by incompleteness existing in the real environment. Interaction with this environment causes irreversible changes in quantum states, which is called decoherence. Environmental decoherence constitutes a major cause of quantum state corruption and can occur not only in quantum memory but also during quantum transmission or quantum processing. Figure 22 illustrates the relationships between quantum channel models widely used for modeling environmental decoherence.

[0334] Environmental decoherence can be described as unwanted interactions between a qubit and its environment, more specifically as entanglement, which disrupts the coherent superposition of the fundamental quantum state. For example, in such cases, the qubit (or quantum system) loses energy due to interaction with the environment; this can be exemplified by the collapse of the qubit's excited state due to spontaneous photon emission, or the loss or absorption of photons 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 a model known as dephasing or phase damping, which is characterized by the loss of quantum information without energy loss and can occur, for instance, in cases of photon scattering or perturbations of electronic states caused by stray charges.

[0335] However, for NQubit systems, amplitude attenuation channel or phase attenuation channel models result in a system that is 2 NBecause it requires a dimensional Hilbert Space, it is not feasible to simulate such channels classically. For efficient classical simulation, the amplitude and phase decay channels are Pauli channels N P It can be approximated as such, and the input state with density operator ρ is mapped to the state as in Equation 3 below.

[0336]

[0337] If one of the qubits in the state passes through the depolarization channel, fidelity Summarized as Werner state W F is defined as in mathematical formula 4 below.

[0338]

[0339] In this case, I, X, Y, and Z correspond to a single Qubit Pauli operator, and p x , p y , p z ε is the probability of Pauli X, Pauli Y, and Pauli Z errors occurring. Bit flip errors corresponding to the Pauli X channel and bit-phase flip errors corresponding to the Pauli Y channel are related to amplitude decay, while phase flip errors corresponding to the Pauli Z channel are caused by phase decay. The most practical quantum system is an asymmetric channel, which is a channel where one of bit flip, phase flip, or bit-phase flip errors predominates. Bit flip, phase flip, and bit-phase flip errors occur with equal probability (p x =p y =p z A special case of a Pauli channel is called a depolarizing channel and can be mathematically expressed as shown in Equation 4 below.

[0340]

[0341] Furthermore, quantum channels sometimes consider not only quantum states but also errors occurring in operators utilized in quantum circuits. The CNOT(=U) operator and measurement error, which are typically considered, are mapped as follows. At this time represents the Pauli I, X, Y, Z operators.

[0342] Mapping of CNOT Errors:

[0343] Mapping of Z-measurement errors:

[0344] Mapping of X measurement errors:

[0345] means the error pattern, and p ij Is It refers to the probability of an error occurring, E0 and E1 refer to measurement operators including measurement error, and p m represents the measurement error rate.

[0346]

[0347] Quantum Entanglement Distillation Protocol (EDP)

[0348] Quantum entanglement distillation protocol is a technique that utilizes multiple entangled states with low fidelity and Local Operator and Classical Communication (LOCC) to share a small number of entangled states with high fidelity among multiple parties.

[0349] Various protocols, such as recurrence, QPA, breeding, and hashing, have been developed as entanglement distillation protocols. Among them, the recurrence and QPA protocols use classical communication bidirectionally from Alice to Bob and from Bob to Alice, while the breeding and hashing protocols use classical communication unidirectionally from Alice to Bob. Generally, bidirectional protocols have the advantage of being able to operate even in poor channel environments compared to unidirectional protocols, while unidirectional protocols have the advantage of utilizing fewer resources compared to bidirectional protocols.

[0350] High-fidelity EPR states or entangled states generated by the entanglement distillation protocol can be utilized for quantum teleportation or direct quantum communication, quantum key distribution, distributed quantum computing, etc.

[0351]

[0352] QPA protocol

[0353] The QPA protocol is a bidirectional protocol that utilizes two EPR states in each round to probabilistically generate a single high-fidelity EPR state. When each particle of an EPR state is shared between the sender and receiver, as the first step, Alice and Bob operate the following unitary operator on the individual qubits of each EPR pair.

[0354]

[0355] The relevant Unitary course and They convert to each other. That is, This is because subsequent operations correct X and Y errors well but do not correct Z errors well, so the Z error of the state after each round is converted into a Y error to correct it in subsequent rounds.

[0356] In the second step, Alice and Bob perform a CNOT operator between the two EPR pairs. Finally, they measure the EPR state that operated as the Target using the Z basis and share the measurement value via classical communication. If Alice and Bob have the same shared measurement value, it is determined that the EPR state operated as the Control qubit has higher fidelity, and that entangled state is utilized thereafter; if the shared measurement values ​​differ, the EPR state operated as the Control qubit is discarded. Therefore, the QPA protocol probabilistically succeeds (preserving the EPR state operated as the Control qubit) based on the measurement result, and the success probability (p succ ) is the initial fidelity (F or F init It is determined by ).

[0357] When the initial EPR state passes through a Symmetric Depolarizing Channel, the entanglement state after the channel can be expressed as shown in the following mathematical equation 7.

[0358]

[0359] Werner state of fidelity F (W F When performing a single round using ), the probability of success is given by the following mathematical formula 8.

[0360]

[0361] FIG. 23 is a diagram illustrating an example of the execution process of the QPA protocol in a system applicable to the present disclosure.

[0362] p succ The entanglement state generated with probability is as shown in the following mathematical formula 9.

[0363]

[0364] Additionally, to achieve high fidelity, each round can be performed repeatedly, and when performing n rounds, at least 2 n The EPR status of dogs is utilized.

[0365] Depending on memory availability in the QPA protocol, each round of the QPA protocol can be connected serially rather than in parallel to operate in pumping mode. When operating in entanglement pumping mode, instead of proceeding to a new round based on the EPR state output from each round, the round proceeds by utilizing the output EPR state of one round and the initial EPR state; thus, there is an advantage in that the number of quantum memories used is reduced. However, if the increase in fidelity is low and fidelity reaches a saturation state, it must be switched to a round output EPR pair.

[0366]

[0367] Recurrence Protocol

[0368] The Recurrence Protocol is a bidirectional protocol similar to the QPA Protocol that utilizes two EPR states in each round to probabilistically generate a single high-fidelity EPR state. Unlike the QPA Protocol, U in the first step A ,U B A non-Unitary Twirling operation corresponding to is performed. As the first step, Alice and Bob probabilistically apply the Twirling operator to the individual qubits of each EPR pair. Equation 10 represents the Twirling operation.

[0369]

[0370] Bx represents the operator for rotating π / 2 around the X-axis, By represents the operator for rotating π / 2 around the Y-axis, and Bz represents the operator for rotating π / 2 around the Z-axis.

[0371] The Twirling process is a process for changing an arbitrary mixed state M into a Werner state W_F.

[0372] In the second step, Alice and Bob perform a CNOT operator between the two EPR pairs. Finally, they measure the EPR state operated as the Target using the Z basis and share the measurement value via classical communication. Alice and Bob determine that the EPR state operated as the Control qubit has higher fidelity based on the shared measurement value and utilize it thereafter; if the shared measurement value differs, the EPR state operated as the Control qubit is discarded. Therefore, the Recurrence protocol succeeds probabilistically based on the measurement result, and the probability of success (p succ ) is determined by the initial fidelity. When a single round is performed using the Werner state of fidelity F, the probability of success is given by the following mathematical formula 11.

[0373]

[0374] Additionally, to achieve high fidelity, each round can be performed repeatedly, and when performing n rounds, at least 2 n The EPR status of dogs is utilized.

[0375] When a single round is performed using the Werner state of fidelity F, the output fidelity F' is equal to the following mathematical formula 12.

[0376]

[0377] Depending on memory availability in the Recurrence protocol, each round of the Recurrence protocol can be connected serially rather than in parallel to operate in pumping mode. When operating in entanglement pumping mode, instead of proceeding to a new round using the EPR state output from each round, the round proceeds by utilizing the output EPR state of one round and the initial EPR state; thus, there is an advantage in that the number of quantum memories used is reduced. However, if the increase in fidelity is low and fidelity reaches a saturation state, it must be switched to a round output EPR pair.

[0378] Due to the difference in the operators used in the first process, when an arbitrary state is used as the input state, it has lower fidelity performance than the QPA protocol.

[0379]

[0380] Double selection protocol

[0381] FIG. 24 is a diagram illustrating an example of the process of the Double selection protocol in a system applicable to the present disclosure.

[0382] The Double Selection Protocol is a bidirectional protocol similar to the Recurrence or QPA protocols, but it generates a single entangled state by utilizing three EPR states instead of two EPR states in each round. (See Fig. 24)

[0383] The execution process of the double selection protocol in a single round is as follows.

[0384] ① The sender and receiver have three entanglement states (ρ (0) ,ρ (1) ,ρ (2) ) among ρ (0) As control, ρ(1) Perform CNOT targeting each.

[0385] ② The sender and receiver are ρ (2) ρ as the control for the qubit at the position (1) Perform CNOT targeting the qubit at each position.

[0386] ③ The sender and receiver are ρ (1) Measure the qubits of each position in the Z basis and ρ (2) Each qubit in the position is measured on an X basis.

[0387] ④ The sender and receiver share the measurement results with each other, and if the sender's X measurement result matches the receiver's X measurement result and the sender's Z measurement result matches the receiver's Z measurement result, then ρ (0) The qubit at the position is preserved. Otherwise, if the X measurement result is inconsistent, the Z measurement result is inconsistent, or both are inconsistent, ρ (0) Discard the qubit at the position.

[0388] When additional rounds are performed, the transmitter and receiver apply a Hadamard operator to each input state, or change the control and target positions of the CNOT and convert the measurement basis, and then repeat steps ① to ④.

[0389] It is known that the Double Selection Protocol has the following advantages compared to QPA or Recurrence Protocols.

[0390] 1) High channel threshold

[0391] 2) High Maximum Fidelity

[0392] 3) Wide gate working range

[0393] When performing multiple rounds, an H operator is applied to each output state, and in this specification, Double selection (Twirling version) means performing a Twirling operator before each round and then performing a Double selection circuit.

[0394]

[0395] Hashing protocol

[0396] FIG. 25 is a diagram illustrating an example of a QECCs-based hashing protocol in a system applicable to the present disclosure.

[0397] The Hashing protocol is a unidirectional protocol that utilizes decoding circuits for quantum error correction codes to distill into an entangled state. In the first step, Alice and Bob perform quantum error correction code encoding and decoding circuits (U_enc^t, U_dec) on each EPR state and measure specific qubits similar to syndrome extraction. In the second step, Alice shares the measurement results with Bob, and Bob performs a correction operator based on the product pattern of his measurement results and Alice's. The correction operator is determined by the correction operator corresponding 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 above a threshold value, the unmeasured EPR state has enhanced fidelity.

[0398] In the case of the EPR state, since it is a CSS state and an H invariant state (CSS-H invariant state) that is closed to the H transformation of a fixed operator, a decoding circuit for an arbitrary error correction code is utilized. Since it is a CSS state such as the 3-qubit GHZ state used in multi-party but an H variant state (CSS-H variant), a decoding circuit for a CSS quantum error correction code must be utilized, and in the case of the Non-CSS state, a CSS-H code, that is, a quantum error correction code in which logical H is expressed in the form of a tensor product of individual (transversal) H, must be utilized.

[0399] CSS state is each fixed operator S i ∈S means a state that can be expressed by one of the Pauli operators (X or Y or Z).

[0400]

[0401] Breeding Protocol

[0402] The Breeding protocol is a unidirectional protocol, similar to the Hashing protocol. In the Breeding protocol This is a protocol for improving n > m low-fidelity EPR states to high-fidelity by utilizing m pure entanglements, where Alice and Bob share n pure entanglements. In this case, to distill a single EPR state, Von-Neumann entropy A 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.

[0403] To execute 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 the m pure EPR states. In the second step, Alice shares the measurement results with Bob, and Bob performs a correction operator based on the product pattern of his measurement results and Alice's measurement results. If the initial fidelity is above a threshold, unmeasured EPR states have a fidelity of 1.

[0404] The fidelity performance of the protocol output value can be determined according to the method of performing the BXOR process, and the BXOR circuit can be determined based on the decoding circuit of the quantum error correction code during the BXOR detection process.

[0405] The Hashing protocol and Breeding protocol described above have a high Yield (the number of EPR states required to obtain one EPR state with Fidelity 1) compared to QPA or Recurrence, and the Yield is expressed as shown in Equation 13 below.

[0406]

[0407] Breeding / Hashing protocols can be utilized with QPA / Recurrence as needed.

[0408]

[0409] Distinction between unidirectional and bidirectional Entanglement Distillation Protocol (EDP)

[0410] The bidirectional (Recurrence or QPA protocol) or unidirectional EDP technique consists of a transceiver, a quantum channel, and a classical channel, as shown in FIGS. 26 and 28.

[0411] (1) Bidirectional EDP technique

[0412] FIG. 26 illustrates an example of the structure of a single round of a bidirectional EDP technique in a system applicable to the present disclosure. Specifically, FIG. 26 shows the basic structure of a single round of a conventional bidirectional EDP technique.

[0413] In a single round, the bidirectional EDP technique operates as follows.

[0414] ① The transmitter (Alice) uses a nonlinear element to generate two EPR states.

[0415] ② The sender transmits one qubit of each EPR state to the receiver (Bob) (a total of 2). At this time, the quantum channel includes the generation defect of the EPR state, wired and wireless photon channels, quantum memory error channels, etc.

[0416] ③ The sender and receiver perform a unitary operation by grouping the two EPR states. In this case, if a Werner state is not generated after the quantum channel, the Werner state W F Unitary operations (U) and CNOT operations are performed to convert to. On the other hand, if a Werner state is generated after the quantum channel, only CNOT operations are performed.

[0417] ④ The sender and receiver measure some qubits. They measure the qubit used as the target qubit for the CNOT operation in ③. FIG. 27 is a diagram illustrating an example of the process of the sender and receiver measuring qubits in a system applicable to the present disclosure.

[0418] ⑤ The receiver sends its measurement result (1 bit) to the sender, and the sender sends its measurement result (1 bit) to the receiver via the Classical Channel.

[0419] 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, discard the EPR state not used for measurement and repeat steps ① through ⑤. If the measurement results match, preserve the corresponding qubit.

[0420] If an EPR state with higher fidelity is required, perform steps ② through ⑥ using the qubits preserved in a single round. When performing n rounds, at least 2 n The EPR state must be shared between the transmitter and receiver, and additional qubits are required based on the measurement results. Finally, one high-fidelity EPR state is generated.

[0421]

[0422] (2) Unidirectional EDP technique

[0423] FIG. 28 is a diagram illustrating an example of the structure of a unidirectional EDP technique in a system applicable to the present disclosure.

[0424] ① In the case of a unidirectional EDP utilizing [[n,k,d]] quantum error correction codes, the transmitter (Alice) uses nonlinear elements to generate n EPR states.

[0425] ② The transmitter sends one qubit from each EPR state to the receiver (Bob) (a total of n). At this time, the quantum channel includes the generation defect of the EPR state, wired and wireless photon channels, quantum memory error channels, etc.

[0426] ③ As a unitary operation, in the case of unidirectional EDP, the sender and receiver perform a decoding circuit or BXOR circuit of the agreed [[n,k,d]] quantum error correction code.

[0427] ④ The sender and receiver each measure (nk) qubits. At this time, the position of the measured qubit is determined based on the characteristics of the BXOR circuit or the [[n,k,d]] quantum error correction code.

[0428] ⑤ The receiver transmits the measurement result ((nk) bits) to the sender via the Classical Channel.

[0429] ⑥ The sender estimates a syndrome or error from the receiver's measurement result and performs a correction operation for error correction on the k qubits that were not measured in ④. The correction operator is determined according to the product pattern of the measurement result, and one of the Pauli operators I, X, Y, Z is operated on the sender's qubit among a pair of EPR states.

[0430]

[0431] Finally, in the case of Recurrence or QPA bidirectional EDP, one high-fidelity EPR state is preserved, and in the case of unidirectional EDP using [[n,k,d]] error correction codes, k high-fidelity EPR states are preserved.

[0432] Quantum entanglement distillation techniques can correct channel errors in the entanglement state shared by Alice and Bob through the above process. This stems from the process of transferring a portion of the entanglement held by a majority of entanglement states to a minority of entanglement states, and classical channels are utilized for this purpose.

[0433] As such, EDP aims to create an entangled state with high fidelity. Currently known QPA / Recurrence protocols have the limitation that they require a lot of resources to generate the target fidelity, and hashing / breeding protocols based on quantum error correction codes have the limitation that they only operate at high initial fidelity.

[0434]

[0435] How the EDP protocol operates

[0436] Representative modes of operation for entanglement distillation protocols include the recurrent mode and the nested entanglement pumping mode.

[0437] Recurrent mode

[0438] FIG. 29 is a drawing illustrating an example of a 4-round recurrent mode in a system applicable to the present disclosure.

[0439] Figure 29 shows the case where a recurrence mode symmetrically utilizing a quantum entanglement distillation protocol that measures one entanglement state using two states is performed four rounds. In the case of the recurrent mode, the Fidelity performance and success probability performance according to the round are superior to those of the Nested entanglement pumping mode.

[0440]

[0441] (Nested) Entanglement pumping mode

[0442] FIG. 30 is a drawing illustrating an example of a (Nested) Entanglement pumping mode in a system applicable to the present disclosure.

[0443] Figure 30 illustrates the case where a quantum entanglement distillation protocol, which utilizes two states to measure one entanglement state, is used in (Nested) Entanglement pumping mode. In the figure, the distillation protocol is performed with an entanglement state having high fidelity after three pumping cycles, where fidelity is saturated. This protocol has the advantage of not utilizing quantum memory.

[0444]

[0445] EDP ​​protocol utilizing Bell diagonal state transitions

[0446] Transformation method of Bell Diagonal state [1,2,10]

[0447] The Bell diagonal state can be transformed according to each unitary as shown in the following mathematical equations 14, 15, 16, and 17.

[0448] ① How to use

[0449]

[0450] At this time is. Through the corresponding operation, While and It is possible to perform conversions between them.

[0451] ② How to use

[0452]

[0453] At this time is. Through the corresponding operation, While It is possible to perform conversions between them.

[0454] ③ How to use

[0455]

[0456] At this time is. Through the corresponding operation, While It is possible to perform conversions between them.

[0457] ④ How to use

[0458]

[0459] At this time is. Through the corresponding operation, While and It is possible to perform conversions between them.

[0460] and You can freely transform Bell diagonal states using operators. Additionally, you can transform Bell diagonal states into Werner states by utilizing Twirling and Pauli Y.

[0461] Also, considering the Global phase, in front of each unitary Includes The same result can be obtained even if it is performed.

[0462]

[0463] 2-1 EDP using Operator Adaptation

[0464] FIG. 31 is a diagram illustrating an example of an EDP execution circuit after a transformation process of a Bell diagonal state utilizing various unitary operations in a system applicable to the present disclosure.

[0465] FIG. 32 is a diagram illustrating an example of a Recurrence protocol (circuit performing EDP after changing the Werner state by twirling) in a system applicable to the present disclosure.

[0466] The Z basis measurement of the CNOT and Target qubits changes the state as shown in Equation 18 below.

[0467]

[0468] That is, the transmitter and receiver are fixed as shown in FIG. 23 above Rather than performing, tailored to the target performance (Fig. 31) or an operator adaptation technique, which is a technique for performing Twirling (Fig. 32), can be performed. Additionally, due to the characteristics of 2-1 EDP performance, Alternatively, choosing between Twirling might be sufficient.

[0469] FIG. 33 is a diagram illustrating an example of the process of executing the 2-1 EDP protocol through operator adaptation in a system applicable to the present disclosure.

[0470] Figure 33 is a diagram illustrating the execution of the 2-1 EDP protocol utilizing operator adaptation. The execution process is as follows.

[0471] ① Determine the coefficients F, F1, F2, and F3 of the W' state through channel estimation.

[0472] ② The transmitter, in accordance with the target output state Or select the appropriate operator O from Twirling.

[0473] ③ The sender generates two EPR states.

[0474] ④ The sender transmits one qubit from each EPR state to the receiver (Bob) (a total of 2). At this time, the transmitted qubit undergoes a quantum channel, and the quantum channel includes the generation defect of the EPR state, wired and wireless photon channels, quantum memory error channels, etc.

[0475] ⑤ The sender and receiver perform operations on each qubit according to the operator O determined in process ②.

[0476] ⑥ Combine the two entangled states and measure the CNOT and the Target qubit of the CNOT using the Z basis.

[0477] ⑦ The transmitter and receiver share the Z basis measurement results.

[0478] ⑧ Compare whether the measurement results are the same,

[0479] If the same, preserve the entanglement state used as the Control qubit, and

[0480] If different, discard the entangled state used as the Control qubit and start again from step ③.

[0481] 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) described in this invention is described in a unified manner for the convenience of explanation. However, it is obvious that this method can be applied equally to 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.

[0482] In addition, although the same W' state was considered as an input value in the present invention, to generate a target output state for different Bell diagonal states as well Alternatively, it is obvious that one can choose the appropriate operator O among Twirling.

[0483]

[0484] Composition of various embodiments of the present disclosure

[0485] The present disclosure proposes a method for designing a mode of EDP based on Yield in a quantum entanglement distillation protocol and a communication protocol for this purpose, and verifies the performance of the protocol through the performance when this is performed using QPA.

[0486]

[0487] The symbols / abbreviations / terms used in this disclosure are as follows.

[0488] - EDP: Entanglement Distillation Protocol

[0489] - LOCC: Local Operator and Classical Communication

[0490] - p succ : Success probability

[0491] - Ftarget Target Fidelity

[0492] - ρ init : EPR pairs transmitted from quantum channel

[0493] - F init : Initial Fidelity of EPR pair transmitted from quantum channel

[0494] - F',F output : output Fidelity of EPR pair after EDP protocol

[0495]

[0496] Technical problem to be solved by the invention

[0497] Low yield due to fidelity difference based on the round of recurrent mode

[0498] In the case of recurrent mode, generally, the benefit of a high increase in Fidelity can be seen with each round. However, there are cases where the difference in output Fidelity with increasing round count is significant, and excessively high Fidelity is obtained compared to the target Fidelity. The following [Table 5] shows the performance of (Nested) Entanglement pumping mode and recurrent mode when utilizing QPA.

[0499] (Nested) Entanglement Pumping QPARecurrent QPAF_initF_init2roundF_outputp_succYield^(-1)F_initF_init2roundF_outputp_succYield^(-1)0.550.5510.560340.583.4482760.550.5510.560340.583.4482760.560340.5520.576090.555178.0124570.560340.5603420.594750.5380512.817680.576090.5530.582060.5695715.823270.594750.5947530.64080.5625145.573160.582060.5540.587150.5671829.661250.64080.640840.688210.60919149.61890.587150.5550.589670.5699953.79260.688210.6882150.77310.61626485.57060.589670.5560.59140.5700896.113880.77310.773160.860540.696441394.4360.59140.5570.592370.57075170.15140.860540.8605470.932680.794563509.9580.592370.5580.592980.57092299.78170.932680.9326880.983050.884937932.7360.592980.5590.593330.57111526.66150.983050.9830590.997210.9690916371.520.593330.55100.593550.57119923.79340.997210.99721100.999840.994632920.80.593550.55110.5936790.5712481618.90.9998350.999835110.9999980.99967265863.21

[0500]

[0501] '(Nested) Entanglement Pumping QPA' refers to the case where the QPA is utilized in (Nested) Entanglement Pumping mode, and 'Recurrent QPA' refers to the case where the QPA is utilized in Recurrent mode.

[0502] 'F_init' and 'F_init2' represent the fidelity of the two input states of the QPA, 'round' represents the number of rounds in which the QPA is executed, and 'F_output' represents the fidelity F' of the output state. 'p_succ' represents the success probability of the protocol, and 'Yield^(-1)' represents the reciprocal of the yield and the average number of qubits utilized. F init F at =0.55 target If performed to satisfy = 0.57, for the (Nested) Entanglement pumping mode, 8.01 ρs init This is necessary, but for the recurrent mode, 12.82 ρs init This is necessary, and an excessively high fidelity (0.59) is obtained compared to the target fidelity (0.57). Therefore, a protocol is needed that utilizes a low entanglement state while appropriately obtaining the target fidelity.

[0503]

[0504] (Nested) Degraded Fidelity and p in Entanglement pumping mode succ performance

[0505] In most cases, the (Nested) Entanglement Pumping mode has the advantage of lower memory usage compared to the recurrent mode, but it exhibits significantly degraded F' performance when using the same number of entanglement states. As shown in [Table 5], the 'Recurrent QPA' demonstrates a fidelity of 0.59475 by utilizing 12.81 entanglement states, whereas the '(Nested) Entanglement Pumping QPA' demonstrates a fidelity of 0.59368 even when utilizing 1618.9 entanglement states. Therefore, a method is needed to improve the output fidelity F' by proposing a method that utilizes the QPA output entanglement states more effectively than the (Nested) Entanglement Pumping mode.

[0506]

[0507] Detailed Description of the Invention

[0508] Presents a mode that generates Target Fidelity more efficiently (High Yield) by changing the connection relationships between Output States.

[0509] FIG. 34 is a diagram illustrating an example of a 4 Round Recurrent protocol and an EDP block number in a system applicable to the present disclosure.

[0510] FIG. 35 is a diagram illustrating an example of a representation of an EDP protocol in which Blocks 3, 7, and 11 are removed in a system applicable to the present disclosure.

[0511] FIG. 36 is a diagram illustrating an example of the connection relationship of EDP blocks in which Blocks 3, 7, and 11 are removed in a system applicable to the present disclosure.

[0512] There are various metrics for evaluating EDP, such as output fidelity, threshold, and yield, and this disclosure presents a technique for designing a mode in a direction that optimizes yield.

[0513] One method for designing the EDP mode is F target By removing the EDP block in the Recurrent mode satisfying , the yield is improved, and F target The fidelity is to be achieved as described above. In the case where the 4-round Recurrent mode is utilized in FIG. 34, there are a total of 15 EDP blocks, and a total of 16 input states are required as inputs. If blocks 3, 7, and 11 are removed as in FIG. 35 and configured as in FIG. 36, 11 input states are required. Thus, the present invention proposes a technique for removing EDP blocks in the structure of the Recurrent Mode in a direction that increases the yield while satisfying the Target Fidelity.

[0514] The location (p) of the EDP block to be removed can be found by solving the optimization problem of Equation 19, which optimizes the yield while satisfying the Target Fidelity, and the location of the EDP block to be removed can be found by utilizing an algorithm such as a qubit location search technique.

[0515]

[0516] A communication protocol that determines the EDP mode based on yield.

[0517] FIG. 37 is a diagram illustrating an example of a communication protocol that determines the EDP mode based on Yield in a system applicable to the present disclosure.

[0518] In this technique, communication for sharing the EDP mode must be utilized additionally compared to existing techniques, and the transmitter and receiver must perform EDP in the EDP mode. Communication for this purpose can be configured as shown in FIG. 37.

[0519] In advance, the sender and receiver are F init ,F target ,n round , shares an EDP type. In this case, the 'EDP type' is a type of EDP such as QPA and double selection.

[0520] ① The sender transmits the quantum entanglement state to the receiver.

[0521] ② The transmitter determines the EDP mode.

[0522] ③ The sender shares the EDP mode with the receiver.

[0523] ④ The sender and receiver perform the EDP circuit based on the shared EDP mode.

[0524] It is self-evident that the EDP mode can be determined by the receiver (Bob) performing ② without the sender performing ②, or by the sender and receiver each performing ②.

[0525]

[0526] (Example) Performance in QPA

[0527] FIG. 38 is a drawing illustrating an example of a Yield Optimal EDP in a system applicable to the present disclosure.

[0528] Fig. 38 is F init =0.8,F target Represents the Yield Optimal EDP at =0.99.

[0529] The performance when utilizing the EDP mode proposed in QPA is as shown in Tables 6 and 7. 'F_init' is F init , 'F_target' is F target, 'Mode' is Recurrent or the proposed method, 'Yield^(-1)' is the reciprocal of Yield, 'F_output' is the fidelity F' of the output state, and 'round' means the longest round (same as the number of rounds in Recurrent mode).

[0530] F init =0.7,F target In the case of =0.99 (Table 6), 21 ρ init Usage can be reduced, and the 21% EPR status is utilized less. F init =0.8,F target In the case of =0.99 (Table 7), 9 ρ init Usage can be reduced, and the 29% EPR status is utilized less. F init =0.8,F target In the case of =0.999 (Table 7), 18 ρ init Usage can be reduced, and the 29% EPR status is utilized less.

[0531] Table 6 is F init F when =0.7 target It shows the yield and fidelity of the Recurrent and proposed method (Prop) according to.

[0532] Table 7 is F init F when =0.8 target It shows the yield and fidelity of the Recurrent and proposed method (Prop) according to.

[0533] F init F target ModeYield^(-1)F output rround0.70.9Recurrent23.98470.9343953Prop20.65370.90578230.95Recurrent53.39520 .9720044Prop47.22160.95578840.99Recurrent112.68720.9969645Prop91.05070.9905855

[0534]

[0535] F init F target SchemeYield^(-1)F'round0.80.9Recurrent15.50350.9879743Prop9.640090.91153130.95Recurrent15.50350.9879743Prop11.7 8880.97252830.99Recurrent31.66650.9968544Prop22.26090.99118140.999Recurrent63.72920.9999355Prop45.31530.9990255

[0536]

[0537] FIG. 39 is a diagram illustrating an example of an Entanglement Distillation Protocol (or Entanglement Purification Protocol, EPP) in a system applicable to the present disclosure.

[0538] FIG. 40 is a drawing illustrating an example of bipartite entanglement purification with single selection in a system applicable to the present disclosure.

[0539] The Entanglement Distillation Protocol (EDP) or Entanglement Purification Protocol (EPP) is a process that converts a low-fidelity impure entanglement state into a high-fidelity pure entanglement state through local unitary operations, measurements, and classical message exchanges.

[0540] In FIG. 39, multiple entangled states are prepared, and these states communicate and interact through a classical channel. Through this process, specific states are selectively maintained or unnecessary states are removed, so that an entangled state of high fidelity remains.

[0541] FIG. 40 illustrates a bipartite entanglement purification process utilizing single selection. This process is intended to improve the entanglement state between two parties (Alice and Bob). Alice and Bob each perform local operations and measurements, receiving entanglement states ρ(0) and ρ(1) as input. Alice performs operations based on the entanglement state and then transmits classical information to Bob. This classical information is used by Bob to adjust his entanglement state and improve the quality of the final state through selective operations. Through this process, low-quality entanglement states can be removed and entanglement states with high fidelity maintained through classical information exchange and state selection.

[0542]

[0543] FIG. 41 is a diagram illustrating an example of an EDP in recurrent mode in a system applicable to the present disclosure.

[0544] FIG. 42 is a drawing illustrating an example of an EDP in pumping mode in a system applicable to the present disclosure.

[0545] There are two types of EDP modes: Recurrent mode and (Nested) Entanglement Pumping mode. Recurrent mode offers excellent performance and a large increase in Fidelity, although it can increase excessively in some cases. Additionally, it requires memory in terms of success probability (p_succ). On the other hand, Pumping mode has relatively lower performance but does not require memory and has the advantage of increasing the resolution of the output Fidelity. The appropriate mode varies depending on the Target Fidelity value; for example, F init When F_target=0.57 at =0.55, Pumping mode is advantageous in terms of Yield, and F target When =0.59, Recurrent mode is more advantageous in terms of Yield.

[0546] Figure 41 illustrates the structure of the Entanglement Distillation Protocol (EDP) performed in Recurrent mode. Recurrent mode is a method that repeatedly performs multiple steps, improving the entanglement state at each step to ultimately generate an entanglement state with high fidelity. Referring to Figure 41, an entanglement state composed of multiple blocks begins in an initial state, showing the process in which each block is processed according to a specific algorithm. At each step, the process proceeds by removing low-quality entanglements or applying new operations to gradually improve quality. This process is performed repeatedly, resulting in a better quality entanglement state after each iteration. The characteristic of Recurrent mode is an iterative approach that continuously improves the quality of the state, maintaining connections between blocks until the end of the protocol to secure a final state with high fidelity.

[0547] Figure 42 illustrates the structure of an entanglement distillation protocol (EDP) performed in pumping mode. Pumping mode is a method that generates an entangled state with high fidelity through iterative selection and state adjustment during the process of distilling the entangled state. Referring to Figure 42, multiple entanglement blocks are connected in the initial state, and the process shows how each block undergoes specific operations to progressively improve its quality. Pumping mode employs strategies to remove other states while maintaining a specific state, or to improve the quality of the entangled state through additional operations. This method adjusts the connections between blocks at each stage and aims to ultimately output an entangled state with high fidelity. A characteristic of Pumping mode is that it selectively preserves or removes states during each iteration, thereby efficiently utilizing resources.

[0548]

[0549] FIG. 43 is a diagram illustrating an example of a method in which some EDP of the Recurrent mode is not performed in a system applicable to the present disclosure.

[0550] Recurrent mode has the disadvantage of lower yield due to Fidelity differences occurring depending on the round. (Nested) Entanglement Pumping mode suffers from Fidelity degradation and success probability (p succ There is a problem with performance degradation. To address this, the problem can be resolved and improved by utilizing a Recurrent mode-based Entanglement Pumping method. Additionally, approaches to increase efficiency can be considered, such as not performing some EDPs in Recurrent mode or omitting the dotted line EDP.

[0551] Figure 43 shows the structure of an Entanglement Distillation Protocol (EDP) that applies a method of not executing some EDP blocks in Recurrent mode. This method aims to reduce resource consumption and increase the efficiency of the overall protocol by omitting operations in specific blocks. Figure 43 illustrates the process of selectively executing or omitting blocks at each stage. Operations are not executed in the parts where blocks are omitted; this is a design intended to eliminate entangled states with low fidelity or unnecessary operations. As a result, resources are saved during the iteration of the protocol, and high-quality entangled states can be generated more efficiently.

[0552]

[0553] FIG. 44 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0554] Figure 44 illustrates a technique for selectively removing Entanglement Distillation Protocol (EDP) blocks to optimize yield. This technique aims to reduce resource consumption by eliminating unnecessary blocks and ultimately achieve a higher yield. The mathematical expression in Figure 44 shows how the yield value Y of each block is calculated based on the relationships between blocks and whether they are removed. At each stage, specific blocks are either removed or retained; removed blocks are considered to have a value of Y=1, allowing the protocol to proceed without additional resource consumption. This method reduces unnecessary operations and increases the efficiency of the overall process. Figure 44 visually represents the dependencies and flow between blocks and shows how other blocks are affected during the process of selectively removing blocks. Through this, optimal block configuration and removal strategies can be designed, ultimately enabling the simultaneous achievement of high fidelity and efficient resource utilization.

[0555]

[0556] FIG. 45 is a diagram illustrating an example of location search using a reuse qubit candidate searching algorithm for a block removed in a system applicable to the present disclosure.

[0557] Figure 45 visually illustrates the process of utilizing the Reuse qubit candidate searching algorithm to locate the Entanglement Distillation Protocol (EDP) blocks to be removed. This algorithm demonstrates a method for selectively determining locations to achieve optimal yield while efficiently recycling resources by removing specific blocks. In Figure 45, each block represents a unit that processes entanglement states, where connections and operations occur between blocks. The algorithm analyzes dependencies between blocks and identifies locations that, even if removed, do not significantly affect the performance of the overall protocol. Through this, resource utilization is enhanced by removing unnecessary blocks or selecting optimized paths.

[0558]

[0559] A technique to remove EDP blocks is used to optimize yield. This technique is F^'>F target The goal is to achieve the highest possible Y while satisfying the conditions. The location of the block to be removed is searched using the Reuse qubit candidate searching algorithm. Removing an EDP block has the same effect as utilizing a Low Fidelity qubit, and the value of F′ decreases as the number of removed blocks increases. When an EDP block is removed, performance is maintained by utilizing the yield prior to the removed block.

[0560] The technique for optimizing yield by removing the EDP block is, F'>F targetThe goal is to achieve the maximum Y while satisfying the conditions. The location of the block to be removed is searched using a reuse qubit candidate searching algorithm, which provides the same effect as utilizing low-fidelity qubits. When removing an EDP block, performance is maintained by utilizing the yield prior to the removed block.

[0561] The search process is as follows.

[0562] (1) When removing 1 EDP block, F'>F target Location p satisfying the condition ₁ After exploring, for each position, a vector It is expressed as i = p ₁ If so, v(i) = 1. Otherwise, v(i) = 0. After forming a vector for all p1, form a set S1 consisting of vectors.

[0563] (2) All k≥2 EDP block removal location search For v' such that, for position j where v'(j)=1, remove the EDP block and F'≥F target Awareness check. F'≥F target If so, set S k Add v' to.

[0564] (3) For k=1 to m, S k After creating, For all vector v, compare the yields by removing the EDP block at position j where v(j)=1, and utilize the connection with the highest yield.

[0565]

[0566] FIG. 46 is a diagram illustrating an example of performance when an EDP block is removed in a system applicable to the present disclosure.

[0567] The table in Figure 46 illustrates the performance difference between Recurrent mode and Prop mode when transitioning from the initial state to the target state. Recurrent mode provides overall stable and high performance, but requires a relatively large number of EPR states to reach the target fidelity. On the other hand, Prop mode enhances efficiency by utilizing techniques to reduce EPR states at specific locations. While this enables resource savings, the yield and fidelity may appear slightly lower compared to Recurrent mode. Prop mode is particularly suitable for maximizing efficiency through the reduction of EPR states and has the advantage of being selectively utilized depending on the target fidelity. These results suggest that the two modes can be used complementarily to balance stability and efficiency.

[0568] FIG. 47 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0569] The technique of removing EDP blocks to optimize yield is F'>F target The goal is to achieve the highest possible Y while satisfying the conditions. The location of the block to be removed is searched using a Reuse qubit candidate searching algorithm, which provides the same effect as using Low Fidelity qubits. Additionally, if an EDP block is removed, performance is maintained by utilizing the yield prior to the removed block. The search process proceeds by finding the optimal removal location while considering performance at each step.

[0570]

[0571] FIG. 48 is a diagram illustrating an example of performance when an EDP block is removed in a system applicable to the present disclosure.

[0572] The table in Fig. 48 is Finit Starting at 0.8, this shows the results comparing the performance of Recurrent mode and Prop mode for different F_target values. Recurrent mode maintains overall stable performance and high fidelity, while Prop mode aims to use resources efficiently by reducing specific EPR states. Prop mode improves resource utilization and achieves optimization through the reduction of EPR states. As target fidelity increases, more EPR state reduction occurs in Prop mode, thereby increasing resource savings. Prop mode is particularly suitable when efficiency is prioritized, and the choice between the two modes is determined by considering the balance between stability and efficiency.

[0573] FIG. 49 is a diagram illustrating an example of a technique for removing EDP blocks to optimize Yield in a system applicable to the present disclosure.

[0574] The technique for optimizing yield by removing the EDP block is, F'>F target The goal is to achieve the maximum Y while satisfying the conditions. The location of the block to be removed is searched using a reuse qubit candidate searching algorithm, which provides the same effect as utilizing low-fidelity qubits. When an EDP block is removed, performance can be maintained or improved by utilizing the yield prior to the removed block. This search process considers performance changes associated with block removal and serves as a strategy to achieve optimal results.

[0575]

[0576] FIG. 50 is a diagram illustrating an example of communication signaling in a technique for removing EDP blocks to optimize yield in a system applicable to the present disclosure.

[0577] The present disclosure provides a technique for removing EDP blocks to optimize Yield. The location of the block to be removed can be searched using a Reuse qubit candidate searching algorithm.

[0578] Figure 50 illustrates the process of optimizing the structure of the Entanglement Distillation Protocol (EDP) and executing it based on it. The first step is to explore an EDP structure that can maximize the yield. Through this, an optimal structure capable of maximizing efficiency and performance is defined. Subsequently, the EDP is executed based on the discovered yield-optimized structure. This process is repeated, and performance is improved by utilizing the optimized structure at each execution stage. The figure shows that this process is systematically cyclical, and as a result, an EDP execution system that provides better performance can be established.

[0579]

[0580] FIG. 51 is a diagram illustrating an example of an EDP protocol in which a Block is removed in a system applicable to the present disclosure.

[0581] Figure 51 visually illustrates the process of removing specific blocks from the Entanglement Distillation Protocol (EDP). Figure 51 explains how the efficiency of the protocol is enhanced by removing unnecessary blocks to optimize the quantum entanglement state. The blocks removed in the figure enable resource conservation and performance optimization through circuit simplification, and the figure systematically analyzes the impact of removing each block on Yield and Fidelity. The connection between the two sides demonstrates that the design ensures the necessary quantum entanglement state is maintained even after block removal. This reflects the protocol's design principles for achieving target performance while reducing complexity.

[0582] FIG. 52 is a diagram illustrating an example of the connection relationship of EDP blocks in a system applicable to the present disclosure.

[0583] Figure 52 illustrates the connection relationships of the Entanglement Distillation Protocol (EDP) blocks and explains how each block interacts and connects during the process of distilling quantum entanglement states. Figure 52 shows how blocks are arranged in series or parallel to optimize the efficiency and stability of the entire protocol through the relationships between the blocks. Connected blocks represent structural paths for data flow and computation, which serve as essential elements for maintaining or enhancing the fidelity of the quantum entanglement state. The figure also emphasizes the importance of inter-block connections by visually representing the impact on the entire connection network when specific blocks are removed or added. This connection structure reflects a systematic design aimed at achieving optimal performance during EDP execution.

[0584] FIG. 53 is a diagram illustrating an example of a communication protocol that determines the EDP mode based on Yield in a system applicable to the present disclosure.

[0585] Figure 53 illustrates the process of a communication protocol that determines the Entanglement Distillation Protocol (EDP) mode based on Yield. Figure 53 visually shows the steps of searching for the optimal EDP mode and executing the protocol based on it, centering on communication between a sender and a receiver. First, the sender searches for an EDP mode that can maximize the Yield and shares the information with the receiver. Subsequently, the sender and receiver execute the protocol based on the shared EDP mode to distill the quantum entanglement state. This process is designed to achieve efficient resource utilization and high fidelity, and emphasizes the clarity and systematic nature of the communication process.

[0586]

[0587] Various embodiments of the present disclosure propose a method for designing EDP modes based on Yield in a Quantum Entanglement Distillation Protocol (EDP) and a communication protocol that supports this. The proposed method verifies performance by utilizing QPA and primarily covers the setting of Yield-based EDP modes and a communication protocol for this purpose. The representative diagram includes a block removal representation of the EDP protocol, inter-block connection relationships, and a communication protocol for determining Yield-based EDP modes.

[0588] In the process, the sender and the receiver are F init ,F target , n round The EDP type is shared in advance. The EDP type is a type of EDP such as QPA or double selection. The sender transmits the quantum entanglement state to the receiver and determines an appropriate EDP mode based on the yield. Subsequently, the determined EDP mode is shared with the receiver, and the sender and receiver execute the EDP circuit based on the shared mode. Through this process, the protocol operates efficiently and provides high performance and stability.

[0589]

[0590] Effects of various embodiments of the present disclosure

[0591] The expected effects of the various embodiments of the present disclosure are as follows.

[0592] According to various embodiments of the present disclosure, a small number of ρ init It is possible to generate an EPR state that satisfies the Target Fidelity by utilizing (using fewer quantum channels).

[0593]

[0594] The characteristic configurations of various embodiments of the present disclosure are as follows.

[0595] (1) A technique for setting the EDP mode based on yield.

[0596] (2) A communication protocol for a technique to set EDP mode based on yield.

[0597]

[0598] [Explanation regarding the 1st node claim]

[0599] The embodiments described above will be explained in detail below with reference to FIG. 54 regarding the operation of the first node. The methods described below are distinguished only for the convenience of explanation, and it is obvious that as long as they are not mutually excluded, a part of one method may be substituted with a part of another method or combined with one another and applied.

[0600] FIG. 54 is a diagram illustrating an example of the operation process of a first node in a system applicable to the present disclosure.

[0601] According to various embodiments of the present disclosure, a method performed by a first node in a communication system is provided.

[0602] 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.

[0603] The embodiment of FIG. 54 may further include, prior to step S5401, one or more of the steps of: the first node receiving one or more synchronization signals from the second node; the first node receiving system information from the second node; the first node receiving configuration information from the second node; and the first node receiving control information from the second node.

[0604] The embodiment of FIG. 54 may further include, prior to step S5401, one or more of the steps of: the first node transmitting a random access preamble to the second node; the first node receiving a random access response (RAR) from the second node; the first node transmitting a random access message 3 to the second node; and the first node receiving a contention resolution message from the second node. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0605] In step S5401, the first node transmits to the second node information regarding the initial fidelity, target fidelity, number of rounds in which the entanglement distillation protocol (EDP) is performed, and the EDP type of the EPR pair (Einstein-Podolsky-Rosen pair).

[0606] In step S5402, the first node transmits information related to the entanglement state of the EPR pair to the second node.

[0607] In step S5403, the first node determines the optimal EDP mode for the optimal EPR yield.

[0608] In step S5404, the first node transmits information of the optimal EDP mode for the optimal EPR yield to the second node.

[0609] In step S5405, the first node performs the EDP based on the optimal EDP mode.

[0610]

[0611] According to various embodiments of the present disclosure, the EPR pair may be a pair of EPR states. The EPR yield may be the reciprocal of the number of EPR states required to obtain one EPR state having the target fidelity. The optimal EDP mode for the optimal EPR yield may be the EDP mode with the highest EPR yield.

[0612] According to various embodiments of the present disclosure, the optimal EDP mode can be determined by removing the EDP block based on the EDP block removal location having the highest EPR yield among the EDP block removal locations where the EDP block can be removed.

[0613] According to various embodiments of the present disclosure, when the EDP block is removed, information on the EPR yield of the removed EDP block may be based on the EPR yield of the EDP block before the removal of the EDP block.

[0614] According to various embodiments of the present disclosure, the step of performing the EDP may include: generating a set number of first EPR states; transmitting one qubit of two qubits constituting each of the first EPR states to the second node; generating a first measurement result by performing the operation of the EDP on the 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.

[0615] According to various embodiments of the present disclosure, the EDP type may be associated with one or more of QPA, double selection, or EDP protocols. The regression mode may be a method of gradually increasing the fidelity of the entanglement state by repeating several rounds until the target fidelity is reached.

[0616]

[0617] 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, and the at least one processor may be configured to perform a method of operation of the first node according to FIG. 54.

[0618]

[0619] According to various embodiments of the present disclosure, an apparatus for controlling a first node in a communication system is provided. The apparatus comprises 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 a method of operation of the first node according to FIG. 54 based on execution by the at least one processor.

[0620]

[0621] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRMs) storing one or more instructions are provided. The one or more instructions perform operations based on execution by one or more processors, and the operations may include a method of operation of a first node according to FIG. 54.

[0622]

[0623] [Explanation regarding the 2nd node claim]

[0624] The embodiments described above will be explained in detail below with reference to FIG. 55 regarding the operation of the second node. The methods described below are distinguished only for convenience of explanation, and it is obvious that as long as they are not mutually excluded, a part of one method may be substituted with a part of another method or combined with one another and applied.

[0625] FIG. 55 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0626] According to various embodiments of the present disclosure, a method performed by a second node in a communication system is provided.

[0627] 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.

[0628] The embodiment of FIG. 55 may further include, prior to step S5501, one or more of the steps of: the second node transmitting one or more synchronization signals to the first node; the second node transmitting system information to the first node; the second node transmitting configuration information to the first node; and the second node transmitting control information to the first node.

[0629] The embodiment of FIG. 55 may further include, prior to step S5501, one or more of the steps of: the second node receiving a random access preamble from the first node; the second node transmitting a random access response (RAR) to the first node; the second node receiving a random access message 3 from the first node; and the second node transmitting a contention resolution message to the first node. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0630] In step S5501, the second node receives information from the first node regarding the initial fidelity of the EPR pair (Einstein-Podolsky-Rosen pair), the target fidelity, the number of rounds in which the EDP (entanglement distillation protocol) is performed, and the EDP type.

[0631] In step S5502, the second node receives information related to the entanglement state of the EPR pair from the second node.

[0632] In step S5503, the second node receives information on the optimal EDP mode for the optimal EPR yield from the first node.

[0633] In step S5204, the second node performs the EDP based on the optimal EDP mode.

[0634]

[0635] According to various embodiments of the present disclosure, the EPR pair may be a pair of EPR states. The EPR yield may be the reciprocal of the number of EPR states required to obtain one EPR state having the target fidelity. The optimal EDP mode for the optimal EPR yield may be the EDP mode with the highest EPR yield.

[0636] According to various embodiments of the present disclosure, the optimal EDP mode can be determined by removing the EDP block based on the EDP block removal location having the highest EPR yield among the EDP block removal locations where the EDP block can be removed.

[0637] According to various embodiments of the present disclosure, the fidelity of the output state may be lowered as the EDP block is removed.

[0638] According to various embodiments of the present disclosure, the step of performing the EDP may include: receiving one qubit from the first node that is one of two qubits constituting each of a set number of first EPR states; generating a second measurement result by performing the operation of the EDP on a second qubit corresponding to some of the first qubits associated with the first EPR states; receiving a first measurement result for the second qubits from the first node; and transmitting the second measurement result to the first node. If the EDP is not performed repeatedly, one or more EPR (Einstein-Podolsky-Rosen) states associated with the result of the EDP may be preserved. If the EDP is performed repeatedly, one or more EPR states associated with the result of the EDP may be discarded.

[0639] According to various embodiments of the present disclosure, the EDP type may be associated with QPA, Double selection, and one of various EDP protocols. The regression mode may be a method of gradually increasing the fidelity of the entanglement state by repeating several rounds until the target fidelity is reached.

[0640]

[0641] 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, and the at least one processor may be configured to perform the operation method of the second node according to FIG. 55.

[0642]

[0643] According to various embodiments of the present disclosure, an apparatus for controlling a second node in a communication system is provided. The apparatus comprises 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 a method of operation of the second node according to FIG. 55 based on execution by the at least one processor.

[0644]

[0645] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRMs) storing one or more instructions are provided. The one or more instructions perform operations based on execution by one or more processors, and the operations may include a method of operation of a second node according to FIG. 55.

[0646]

[0647] Communication systems applicable to the present disclosure

[0648] FIG. 56 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0649] Referring to FIG. 56, 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 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 Thing) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with wireless communication capabilities, an autonomous vehicle, a vehicle capable of performing inter-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 can be implemented in the form of HMDs (Head-Mounted Devices), HUDs (Head-Up Displays) equipped in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. Portable devices may include smartphones, smartpads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.). Home appliances may include TVs, refrigerators, washing machines, etc. IoT devices may include sensors, smart meters, etc. For example, base stations and networks may be implemented as wireless devices, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0650] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) through 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) may communicate with each other through the base station (200) / network (300), but they may 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). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0651] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (200) and base station (200) / base station (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 inter-base station communication (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 / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of various embodiments of the present disclosure, at least some of the following may be performed: 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.), resource allocation processes, etc.

[0652] Meanwhile, NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, when the SCS is 15 kHz, it supports a wide area in traditional cellular bands; when the SCS is 30 kHz / 60 kHz, it supports dense-urban, lower latency, and wider carrier bandwidth; and when the SCS is 60 kHz or higher, it supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0653] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges may change, for example, the frequency ranges of the two types (FR1, FR2) may be as shown in Table 8 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 may mean "sub 6GHz range" and FR2 may mean "above 6GHz range" and may be referred to as millimeter wave (mmW).

[0654]

[0655] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0656]

[0657] As described above, the numerical values ​​of the frequency range of the NR system may change. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 9 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, the frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, for example, for communication for vehicles (e.g., autonomous driving).

[0658] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0659]

[0660] According to various embodiments of the present disclosure, the communication system (1) may support terahertz (THz) wireless communication. THz wireless communication is 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, which has low propagation loss due to molecular absorption in the air.

[0661]

[0662] Wireless devices applicable to the present disclosure

[0663] Hereinafter, examples of wireless devices to which various embodiments of the present disclosure are applied will be described.

[0664] FIG. 57 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0665] Referring to FIG. 57, 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)} may correspond to {wireless device (100x), base station (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 56.

[0666] The first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memory (104) and / or transceivers (106) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed herein. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (106). Additionally, the processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and then store information obtained from the 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 store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement 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 through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be combined with an RF (Radio Frequency) unit. In various embodiments of the present disclosure, the wireless device may refer to a communication modem / circuit / chip.

[0667] The second wireless device (200) includes one or more processors (202) and one or more memories (204), and may additionally include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memory (204) and / or transceivers (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and then store information obtained from the signal processing of the fourth information / signal in the memory (204). Memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, memory (204) may store software code containing instructions for performing some or all of the processes controlled by the processor (202) or for performing the descriptions, functions, procedures, proposals, methods, and / or sequences of operation disclosed in this document. Here, the processor (202) and memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through one or more antennas (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with an RF unit. In various embodiments of this disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0668] Hereinafter, 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 Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation 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 flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document.

[0669] One or more processors (102, 202) may be referred to as a controller, microcontroller, microprocessor, or 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 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. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained in one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0670] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, code, instructions, and / or commands. One or more memories (104, 204) may be composed of ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0671] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this document to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and receive wireless signals. For example, one or more processors (102, 202) may 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 connected 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, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through 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 the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (102, 202).One or more transceivers (106, 206) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (102, 202) from baseband signals to RF band signals. To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters.

[0672] FIG. 58 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0673] According to FIG. 58, 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).

[0674] The difference between the example of the wireless device described in FIG. 57 and the example of the wireless device in FIG. 58 is that in FIG. 57, the processor (102, 202) and the memory (104, 204) are separated, whereas in the example of FIG. 58, the memory (104, 204) is included in the processor (102, 202).

[0675] Here, since the specific descriptions of the processor (102, 202), memory (104, 204), transceiver (106, 206), and one or more antennas (108, 208) are as described above, the descriptions of the repeated descriptions will be omitted to avoid unnecessary repetition of descriptions.

[0676] Hereinafter, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0677] FIG. 59 illustrates a signal processing circuit for a transmission signal.

[0678] Referring to FIG. 59, 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 operation / function of FIG. 59 may be performed in the processor (102, 202) and / or transceiver (106, 206) of FIG. 57. The hardware elements of FIG. 59 may be implemented in the processor (102, 202) and / or transceiver (106, 206) of FIG. 57. For example, blocks 1010 through 1060 may be implemented in the processor (102, 202) of FIG. 57. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 57, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 57.

[0679] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 59. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transmission block (e.g., UL-SCH transmission block, DL-SCH transmission block). The wireless signal can be transmitted through various physical channels (e.g., PUSCH, PDSCH).

[0680] Specifically, a codeword can be converted into a scrambled bit sequence by a scrambler (1010). The scrambled sequence used for scrambling is generated based on an initialization value, which may include ID information of a 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 an N*M precoding matrix W. 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 the complex modulation symbols. Additionally, the precoder (1040) can perform precoding without performing transform precoding.

[0681] A resource mapper (1050) can map the modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. A signal generator (1060) generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) may include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0682] The signal processing process for a received signal in a wireless device can be configured as the inverse of the signal processing process (1010–1060) of FIG. 59. For example, a wireless device (e.g., 100, 200 in FIG. 57) can receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal can be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module. Subsequently, the baseband signal can be restored into a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scrambling process. The codeword can be restored into the original information block 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.

[0683] Hereinafter, examples of wireless device applications to which various embodiments of the present disclosure are applied will be described.

[0684] FIG. 60 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-example / service (see FIG. 56).

[0685] Referring to FIG. 60, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 57 and may be composed of various elements, components, units / parts, 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 additional elements (140). The communication unit may include a communication circuit (112) and 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. 57. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 57. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and additional elements (140) and controls the general operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (130). Additionally, the control unit (120) may transmit information stored in the memory unit (130) to an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0686] The additional element (140) can be configured in various ways depending on the type of 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. 56, 100a), a vehicle (Fig. 56, 100b-1, 100b-2), an XR device (Fig. 56, 100c), a portable device (Fig. 56, 100d), a home appliance (Fig. 56, 100e), an IoT device (Fig. 56, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 56, 400), a base station (Fig. 56, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0687] In FIG. 60, 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 a portion 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 the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). Additionally, each element, component, unit / part, and / or module within the wireless device (100, 200) may include one or more additional elements. For example, the control unit (120) may be composed of one or more sets of 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.

[0688] Hereinafter, an example of the implementation of FIG. 60 will be described in more detail with reference to the drawings.

[0689] FIG. 61 illustrates a portable device applicable to various embodiments of the present disclosure. The portable device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch, smart glasses), a portable computer (e.g., a laptop, etc.). The portable device may be referred to as an MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS (Advanced Mobile Station), or WT (Wireless terminal).

[0690] Referring to FIG. 61, 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 part of the communication unit (110). Blocks 110 to 130 / 140a to 140c each correspond to blocks 110 to 130 / 140 of FIG. 60.

[0691] 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 the components of the portable device (100) to perform various operations. The control unit (120) may include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / code / commands required for the operation of the portable device (100). Additionally, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the portable device (100) and may include wired / wireless charging circuits, batteries, etc. The interface unit (140b) can support the connection between the portable device (100) and other external devices. The interface unit (140b) may include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can receive or output video information / signals, audio information / signals, data, and / or information input by 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, etc.

[0692] For example, in the case of data communication, the input / output unit (140c) acquires information / signals (e.g., touch, text, voice, image, video) input from the user, and the acquired 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 another wireless device or to a base station. Additionally, the communication unit (110) can receive wireless signals from another wireless device or base station and then restore the received wireless signals to their 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).

[0693] FIG. 62 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0694] Vehicles or autonomous vehicles can be implemented as mobile robots, vehicles, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0695] Referring to FIG. 62, 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 part of the communication unit (110). Blocks 110 / 130 / 140a to 140d each correspond to blocks 110 / 130 / 140 of FIG. 60.

[0696] 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, roadside base stations (Roadside units), etc.), and servers. The control unit (120) can perform various operations by controlling elements of the vehicle or autonomous vehicle (100). The control unit (120) may include an Electronic Control Unit (ECU). The driving unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The driving unit (140a) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and may include wired / wireless charging circuits, batteries, 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 inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse 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 technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0697] 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 path and a driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or the autonomous vehicle (100) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, 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 path and the driving plan based on the newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles.

[0698] FIG. 63 illustrates a vehicle applicable to various embodiments of the present disclosure. The vehicle may also be implemented as a means of transport, a train, an aircraft, a ship, etc.

[0699] Referring to FIG. 63, 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. 60, respectively.

[0700] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles or base stations. The control unit (120) can control the 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 within the memory unit (130). The input / output unit (140a) may include a HUD. The position measurement unit (140b) can acquire position information of the vehicle (100). The position information may include absolute position information of the vehicle (100), position information within the driving line, acceleration information, position information relative to surrounding vehicles, etc. The position measurement unit (140b) may include GPS and various sensors.

[0701] For example, the communication unit (110) of the vehicle (100) can receive map information, traffic information, etc. from an external server and store it in the memory unit (130). The location measurement unit (140b) can acquire vehicle location information through GPS and various sensors and store it in the memory unit (130). The control unit (120) creates a virtual object based on map information, traffic information, and vehicle location information, etc., and the input / output unit (140a) can display the created virtual object on the glass window inside the vehicle (1410, 1420). In addition, the control unit (120) can determine whether the vehicle (100) is operating normally within the driving line based on the vehicle location information. If the vehicle (100) deviates abnormally from the driving line, the control unit (120) can display a warning on the glass window inside the vehicle through the input / output unit (140a). Additionally, the control unit (120) can broadcast a warning message regarding a driving abnormality to surrounding vehicles through the communication unit (110). Depending on the situation, the control unit (120) can transmit the vehicle's location information and information regarding the driving / vehicle abnormality to relevant authorities through the communication unit (110).

[0702] FIG. 64 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) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc.

[0703] Referring to FIG. 64, 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. 60, respectively.

[0704] The communication unit (110) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, mobile devices, or media servers. The media data may include video, images, sound, etc. The control unit (120) can perform various operations by controlling the components of the XR device (100a). For example, the control unit (120) may be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation, and processing. The memory unit (130) may store data / parameters / programs / codes / commands required for driving the XR device (100a) or creating an XR object. The input / output unit (140a) acquires control information, data, etc. from the outside and can output the created XR object. The input / output unit (140a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. The sensor unit (140b) can obtain XR device status, surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc. The power supply unit (140c) supplies power to the XR device (100a) and may include a wired / wireless charging circuit, a battery, etc.

[0705] For example, the memory unit (130) of the XR device (100a) may contain information (e.g., data, etc.) necessary for creating an XR object (e.g., AR / VR / MR object). The input / output unit (140a) may receive 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 operation command. For example, if the user intends to watch movies, news, etc. through the XR device (100a), the control unit (120) may transmit content request information to another device (e.g., mobile device (100b)) or a media server through the communication unit (130). The communication unit (130) may download / stream content such as movies, news, etc. from another device (e.g., 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 the content, and can generate / output an XR object based on information about the surrounding space or real object acquired through the input / output unit (140a) / sensor unit (140b).

[0706] Additionally, 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 acquire three-dimensional position information of the mobile device (100b), and then generate and output an XR object corresponding to the mobile device (100b).

[0707] FIG. 65 illustrates a robot applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, domestic, military, etc., depending on the purpose or field of use.

[0708] Referring to FIG. 65, 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. 60, respectively.

[0709] 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 the 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 acquire information from outside the robot (100) and output information to outside the robot (100). The input / output unit (140a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. 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 accelerometer, a magnetic sensor, a gyroscope, 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 movements, such as moving robot joints. Additionally, the driving unit (140c) may enable the robot (100) to travel on the ground or fly in the air. The driving unit (140c) may include an actuator, a motor, a wheel, a brake, a propeller, etc.

[0710] FIG. 66 illustrates an AI device applicable to various embodiments of the present disclosure.

[0711] AI devices can be implemented as stationary devices 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, vehicles, etc.

[0712] Referring to FIG. 66, 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 each correspond to blocks 110 to 130 / 140 of FIG. 60.

[0713] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning model, control signal, etc.) with external devices such as other AI devices (e.g., FIG. 56, 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 an external device or transmit signals received from an external device to the memory unit (130).

[0714] The control unit (120) can 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. The control unit (120) can perform the determined operation by controlling the components of the AI ​​device (100). For example, the control unit (120) can request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and can control the components of the AI ​​device (100) to execute a predicted operation or an operation determined to be desirable among at least one executable operation. Additionally, the control unit (120) can collect historical information, including the operation content of the AI ​​device (100) or user feedback regarding the operation, and store it in the memory unit (130) or the learning processor unit (140c), or transmit it to an external device such as an AI server (Fig. 56, 400). The collected historical information can be used to update the learning model.

[0715] 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 from the learning processor unit (140c), and data obtained from the sensing unit (140). Additionally, the memory unit (130) can store control information and / or software code required for the operation / execution of the control unit (120).

[0716] The input unit (140a) can acquire various types of data from outside the AI ​​device (100). For example, the input unit (120) can acquire training data for model training and input data to which the training model is applied. The input unit (140a) may include a camera, a microphone and / or a user input unit, etc. The output unit (140b) can generate output related to visual, auditory, or tactile senses, etc. 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), surrounding environment information of the AI ​​device (100), and user information using various sensors. The sensing unit (140) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc.

[0717] The learning processor unit (140c) can train a model composed of an artificial neural network using training data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 56, 400). The learning processor unit (140c) can process information received from an external device through the communication unit (110) and / or information stored in the memory unit (130). Additionally, the output value of the learning processor unit (140c) can be transmitted to / be transmitted to an external device through the communication unit (110) and / or stored in the memory unit (130).

[0718] The claims described in various embodiments of the present disclosure may be combined in various ways. For example, the technical features of the method claims of various embodiments of the present disclosure may be combined to be implemented as a device, and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a device, and the technical features of the method claims and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a method.

Claims

1. In a method performed by the first node, A step of transmitting to the second node information related to the initial fidelity, target fidelity, number of rounds in which the entanglement distillation protocol (EDP) is performed, and the EDP type of the EPR pair (Einstein-Podolsky-Rosen pair); A step of transmitting information related to the entanglement state of the EPR pair to the second node; A step of determining the optimal EDP mode for the optimal EPR yield; A step of transmitting information of the optimal EDP mode for the optimal EPR yield to the second node; A step comprising performing the EDP based on the above optimal EDP mode, method.

2. In Paragraph 1, The above EPR pair is a pair in the EPR state, and The EPR yield is the inverse of the number of EPR states required to obtain one EPR state having the above target fidelity, method.

3. In Paragraph 2, The optimal EDP mode for the optimal EPR yield is the EDP mode with the highest EPR yield, method.

4. In Paragraph 1, The above optimal EDP mode is determined by removing the EDP block based on the EDP block removal location having the highest EPR yield among the EDP block removal locations where the EDP block can be removed, method.

5. In Paragraph 4, When the above EDP block is removed, the information on the EPR yield of the removed EDP block is based on the EPR yield of the EDP block before the removal of the EDP block. method.

6. In Paragraph 1, The step of performing the above EDP 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 the EDP operation 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; A step comprising receiving a second measurement result for the second qubits from the second node, method.

7. In Paragraph 1, The above EDP type is associated with one or more of QPA, double selection, or EDP protocols, method.

8. In a method performed by the second node, A step of receiving information from a first node regarding the initial fidelity of an EPR pair (Einstein-Podolsky-Rosen pair), target fidelity, the number of rounds in which an EDP (entanglement distillation protocol) is performed, and the EDP type; A step of receiving information related to the entanglement state of the EPR pair from the second node; A step of receiving information of an optimal EDP mode for an optimal EPR yield from the first node; A step comprising performing the EDP based on the above optimal EDP mode, method.

9. In Paragraph 8, The above EPR pair is a pair in the EPR state, and The EPR yield is the inverse of the number of EPR states required to obtain one EPR state having the above target fidelity, method.

10. In Paragraph 9, The optimal EDP mode for the optimal EPR yield is the EDP mode with the highest EPR yield, method.

11. In Paragraph 8, The above optimal EDP mode is determined by removing the EDP block based on the EDP block removal location having the highest EPR yield among the EDP block removal locations where the EDP block can be removed, method.

12. In Paragraph 11, When the above EDP block is removed, the information on the EPR yield of the removed EDP block is based on the EPR yield of the EDP block before the removal of the EDP block. method.

13. In Paragraph 8, The step of performing the above EDP is: A step of receiving one qubit among two qubits constituting each of the first EPR states of a set number from the first node; A step of generating a second measurement result by performing the EDP operation on second qubits corresponding to some of the first qubits associated with the first EPR states; A step of receiving a first measurement result for the second qubits from the first node; It includes the step of transmitting the second measurement result to the first node, and If the above EDP is not repeated, one or more EPR (Einstein-Podolsky-Rosen) states associated with the result of the above EDP are preserved, and When the above EDP is performed repeatedly, one or more EPR states related to the result of the above EDP execution are discarded, method.

14. In Paragraph 8, The EDP type is associated with one or more of QPA, double selection, or EDP protocols, method.

15. In a first node of a communication system, Transmitter / Receiver; At least one processor; and It includes at least one memory that is operablely connectable to the at least one processor and stores instructions for performing operations when executed by the at least one processor. The above operations are, Comprising all steps of the method according to any one of claims 1 to 7, Node 1.

16. In a second node of a communication system, Transmitter / Receiver; At least one processor; and It includes at least one memory that is operablely connectable to the at least one processor and stores instructions for performing operations when executed by the at least one processor. The above operations are, Comprising all steps of the method according to any one of claims 8 through 14, Node 2.

17. A control device for controlling a first node in a communication system, At least one processor; and It includes at least one memory operably connected to the above at least one processor, and The above at least one memory stores instructions for performing operations based on execution by the above at least one processor, and The above operations are, Comprising all steps of the method according to any one of claims 1 to 7, controller.

18. In a control device for controlling a second node in a communication system, At least one processor; and It includes at least one memory operably connected to the above at least one processor, and The above at least one memory stores instructions for performing operations based on execution by the above at least one processor, and The above operations are, Comprising all steps of the method according to any one of claims 8 through 14, controller.

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

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

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