Error management methods, electronic devices, and storage media for quantum networks

Abstract

This application provides an error management method, electronic device, and storage medium for quantum networks, addressing the problems of low reliability and unadjustable accuracy and throughput in error management of related quantum networks. The method includes, in each execution round of the quantum network protocol, the receiver performs a reliability assessment of error symptom information based on measurement, and a filter determines whether to accept or reject the current round; the filter is used to filter out high-noise rounds; when the filter outputs an acceptance decision, the decoder generates a corresponding recovery operation based on the same error symptom information and performs error correction on the quantum state; when the filter outputs a rejection decision, the current quantum state is discarded and the protocol is restarted. This improves the reliability of the quantum network. Furthermore, by filtering before error correction and adjusting according to the acceptance probability, a controllable trade-off between throughput and reliability is achieved.

Description

Technical Field

[0001] This invention relates to the field of quantum network technology, and more particularly to an error management method, electronic device, and storage medium for quantum networks. Background Technology

[0002] Quantum networks are currently considered a crucial infrastructure for realizing distributed quantum computing, quantum key distribution, and large-scale interconnection of quantum systems. However, quantum information is highly susceptible to noise interference during its preparation, storage, and transmission, including quantum gate errors, measurement errors, decoherence, and transmission noise.

[0003] Therefore, quantum network protocols in related technologies typically draw on schemes from quantum information theory to achieve highly reliable quantum information transmission in quantum networks. These mainly include error detection schemes and error correction schemes. The error detection scheme measures the encoded quantum state; when an error is detected, it is discarded and the protocol is re-executed. Its advantage is its simplicity and lack of complex decoding, but its disadvantage is that its success probability decreases rapidly with the increase in the encoding scale or the number of measurement rounds. The error correction scheme actively corrects errors through decoding algorithms, reducing the logical error rate to some extent. However, in the high-noise environment faced by quantum networks, relying solely on error correction often fails to achieve the reliability levels required for practical applications.

[0004] In summary, the related technologies suffer from low reliability in error management of quantum networks. Summary of the Invention

[0005] This application provides an improved error management method, electronic device, and storage medium for quantum networks.

[0006] This application provides an error management method for quantum networks, including: In each execution round of the quantum network protocol, the receiver performs a reliability assessment on the error symptom information obtained from measurement, and a filter determines whether to accept or reject the current round; the filter is used to filter out rounds with high noise. When the filter output accepts a decision, the decoder generates a corresponding recovery operation based on the same error symptom information and performs an error correction operation on the quantum state. When the filter outputs a rejection decision, the current quantum state is discarded and the protocol is restarted.

[0007] Furthermore, based on the error symptom information obtained through measurement, the receiver performs a reliability assessment on the error symptom information, and the filter makes an acceptance or rejection decision for the current round, including: Based on the current protocol description, the decision rules corresponding to both the protocol description and the expected acceptance probability are established with the decision rules to obtain the corresponding decision rules. Based on the decision rules corresponding to the protocol description and the expected acceptance probability, the reliability of the error symptom information is assessed, and the filter determines whether to accept or reject the current round.

[0008] Furthermore, based on the error symptom information obtained through measurement, the receiver performs a reliability assessment on the error symptom information, and the filter makes an acceptance or rejection decision for the current round, including: The current error symptom information is quantitatively evaluated based on a preset scoring function to obtain the corresponding score value; Determine the corresponding scoring threshold based on the agreement description and the expected acceptance probability; The score is compared with a score threshold, and when the score meets a preset decision condition, an acceptance decision is output. When the score does not meet the preset decision conditions, a rejection decision is output.

[0009] Furthermore, the scoring threshold is set or dynamically adjusted based on the expected acceptance probability or the protocol's expected throughput.

[0010] Furthermore, the scoring function is constructed by the filter based on the number of checks triggered in the error symptom information; The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: After completing the stabilization sub-measurement, a set of error symptom information bits is obtained; each bit is used to indicate whether the corresponding check has been triggered. The filter determines the score for the current round based on the number of checks triggered in the error symptom information. When the score value is higher than the score threshold, the filter outputs an acceptance decision; the acceptance decision is used to allow the decoder to generate a recovery operation and correct the quantum state based on the same error symptom information; When the score value is lower than the score threshold, the filter outputs a rejection decision; the rejection decision is used to discard the quantum state of the current round and trigger a protocol restart.

[0011] Furthermore, the decoder is a minimum weight matching decoder; The step of determining the score for the current round based on the number of triggered checks in the error symptom information through the filter includes: The filter compares the decoding costs generated under different logical assumptions to determine the differences between the decoding costs, which are then used as a score.

[0012] Furthermore, the scoring function is constructed by the filter based on the decoder's processing results of the error symptom information; The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: After obtaining the error symptom information, the decoder is invoked to process the error symptom information; The score for the current round is determined by a filter based on the decoding information output by the decoder during the generation of the recovery operation; the score for the current round is used to reflect the distinguishability or reliability level of the error symptom information under different logical assumptions. When the score value is higher than the score threshold, the filter outputs an acceptance decision, and the decoder generates a recovery operation and performs an error correction operation based on the error symptom information. When the score value is lower than the score threshold, the filter outputs a rejection decision, the current quantum state is discarded, and the protocol is restarted.

[0013] Furthermore, the decoder is a neural network-based decoder; the neural network outputs a value used to characterize the probability of a logical error occurring or the prediction confidence level. The score is determined by the filter based on the value output by the neural network.

[0014] Furthermore, the scoring function used in the filter to calculate the score value of the current round includes at least one of the following: a function based on statistical features of error symptom information, a function based on decoding cost differences, and a function based on the output confidence of a probability model or machine learning model.

[0015] This application provides an electronic device including one or more processors for implementing the method described in any of the preceding claims.

[0016] This application provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the method described in any of the preceding claims.

[0017] This application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described in any of the preceding claims.

[0018] In some embodiments, the error management method for quantum networks of this application performs a reliability assessment on the error symptom information, filters out high-noise rounds, and makes an acceptance or rejection decision for the current round, thereby effectively identifying the transmission round, reducing transmission noise and interference, and improving the reliability of the quantum network. Furthermore, filtering is performed before error correction and adjusted according to the acceptance probability, thereby achieving a controllable trade-off between throughput and reliability to address the problem of unadjustable accuracy and throughput. Attached Figure Description

[0019] Figure 1 The diagram shown is a flowchart illustrating an error management method for quantum networks provided in an embodiment of this application. Figure 2 As shown Figure 1 The diagram shows the interaction flow between the receiver and sender in an error management method for quantum networks. Figure 3 As shown Figure 1 The diagram shows a flowchart illustrating the internal structure of a filter in an error management method for quantum networks. Figure 4 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0021] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0022] The technical solutions of these different concepts will be introduced in turn below. Some terms and concepts may appear in multiple technical solutions of different concepts. These terms and concepts will be explained when they first appear in this article and will not be repeated thereafter.

[0023] Quantum computing (QC) is a computing method that utilizes the properties of quantum states, such as superposition and entanglement, to complete computational tasks.

[0024] A quantum network (QN) is a network consisting of multiple quantum nodes and quantum and classical channels between them, used to realize the remote distribution and processing of quantum states or quantum entanglement.

[0025] A quantum bit (Q) is a form of quantum information carrier, whose state can be in a superposition and entanglement state that classical bits do not possess.

[0026] Quantum operation (QO) refers to the way in which qubits are manipulated to process the quantum information carried by qubits, including quantum gates, qubit state preparation, quantum measurement, etc.

[0027] A quantum gate (QG) is a class of quantum operations that can be represented as unitary transformations between the states of a qubit. A common quantum gate is the Pauli algorithm. , , , Gate, Hadama Gate ( Controlled Pauli X gate (CNOT), swap gate (SWAP), etc.

[0028] Clifford gates are a special type of quantum gate that, under conjugation, convert Pauli gates... , , , The gate is still mapped to a Pauli gate, i.e., denoted as... It's Pauli. If it's the Clifford Gate, then... It's still Pauli Gate.

[0029] A quantum circuit (QC) is a model that describes the process of quantum computing and how quantum operations are performed on qubits.

[0030] Quantum measurement (QM) refers to the classical description of a quantum bit’s state obtained from it.

[0031] Quantum error-correcting code (QECC) is a type of coding scheme that uses logical quantum information encoded on multiple physical qubits to detect and / or correct quantum errors.

[0032] Stabilizer codes (SCs) are an important class of quantum error-correcting codes whose coding space is defined by a set of pairwise commuting stable sub-operators, and whose coding process can be implemented using only Clifford gates.

[0033] Surface codes (SCs) are a type of stable quantum error-correcting code based on a two-dimensional planar lattice structure. Their physical qubits are typically arranged on two-dimensional lattice points or edges, and quantum errors are detected through local stabilizer measurements. Surface codes have advantages such as relying only on nearest-neighbor interactions, high thresholds, and regular implementation structures, and are therefore widely considered one of the mainstream quantum error-correcting code schemes suitable for current and short-to-medium-term quantum hardware conditions.

[0034] Error Syndrome (ES) refers to a classical bit sequence obtained by measuring the stabilizer or check operator, which reflects possible errors in a quantum state.

[0035] Error detection (ED) is an error management method that measures error symptoms and determines whether an error exists, thereby deciding whether to discard the current quantum state.

[0036] Error correction (EC) is an error management method that infers and applies recovery operations based on error symptom information to correct errors in quantum states.

[0037] Hybrid Error Management (HEM) is an error management strategy that combines error detection with error correction, and adaptively decides whether to perform correction or discard the error based on error symptom information.

[0038] A filter is a module that outputs a binary decision based on error symptom information, used to determine whether the current protocol round is accepted.

[0039] A decoder is a module that outputs recovery operations based on error symptom information, used to perform error correction in accepted rounds.

[0040] Acceptance probability (AP) refers to the proportion of rounds in which the filter determines acceptance in multiple protocol execution rounds, and is used to characterize the effective throughput of the system.

[0041] The logical error rate (LER) is the probability that a logical quantum state still has an error after error management and recovery operations have been performed.

[0042] To address the low reliability issue in error management of quantum networks mentioned above, this application provides an error management method for quantum networks. This method assesses the reliability of error symptom information, uses a filter to remove high-noise rounds, and determines whether to accept or reject the current round, thereby effectively identifying transmission rounds, reducing transmission noise and interference, and improving the reliability of the quantum network. Furthermore, filtering is performed before error correction and adjusted according to the acceptance probability, achieving a controllable trade-off between throughput and reliability, thus resolving the problem of unadjustable accuracy and throughput.

[0043] Figure 1 The diagram shown is a flowchart illustrating an error management method for quantum networks provided in an embodiment of this application.

[0044] like Figure 1 As shown, the error management method for quantum networks may include, but is not limited to, the following steps 110 to 130: Step 110: In each execution round of the quantum network protocol, the receiver performs a reliability assessment on the error symptom information based on the error symptom information obtained by measurement, and the filter determines whether to accept or reject the current round; the filter is used to filter out rounds with high noise.

[0045] The filter performs a reliability assessment based on error symptom information, filtering out high-noise rounds. High-noise rounds are those with a high probability or relatively high likelihood of being considered high-noise rounds. In this way, filtering out some high-noise rounds and retaining others results in higher accuracy in identifying the remaining rounds.

[0046] In one example, "high probability" refers to a high-noise round with a probability higher than a preset threshold. This preset probability can be derived empirically. Thus, setting the preset probability to a high value rigorously filters out rounds that may contain high noise. The filter compares measured error symptom information with the preset probability threshold. If the probability that the current round is a high-noise round is higher than the preset probability, the round is rejected and not used for subsequent quantum state processing or applications; otherwise, the round is accepted and allowed to proceed to the next stage of the process. In this way, the confidence level of the current round can be estimated, thereby determining whether to accept or discard it.

[0047] In another example, higher probability means that this round is the most likely to be high-noise compared to other high-noise rounds.

[0048] If there are multiple candidate rounds, the filter compares the high-noise probabilities corresponding to each round and selects the round with the lowest probability value as the accepted round, thereby reducing the interference of high-noise rounds on subsequent processing.

[0049] Step 120: When the filter output accepts the decision, the decoder generates a corresponding recovery operation based on the same error symptom information and performs an error correction operation on the quantum state to correct the error.

[0050] The specific implementation of the decoder in this specification may vary, including but not limited to the least weight matching decoder, and decoders based on neural networks or other learning models.

[0051] The different filters and decoders in this application can be flexibly combined, and have good scalability and versatility.

[0052] Step 130: When the filter outputs a rejection decision, discard the current quantum state and trigger a protocol restart.

[0053] In each round of the quantum network protocol execution, the receiver performs steps 110 to 130 as described above. For ease of overall understanding, let's consider... Figure 2 As shown Figure 1 The interaction flowchart between the receiver and sender in the error management method for quantum networks is illustrated below: First, the sender and receiver determine the protocol parameters used for quantum network communication, including the type of quantum error correction code, the encoding method, and the method of transmitting quantum information.

[0054] Secondly, the sender encodes the quantum information to be transmitted according to the protocol parameters, forms a logical quantum state, and transmits it to the receiver through a quantum channel.

[0055] Next, the receiver performs a stabilization measurement on the received logical quantum state to obtain the corresponding error symptom information.

[0056] Finally, the receiver inputs the error symptom information into the filter, and based on the decision result output by the filter, selects one of the following operations: (1) When the filter output accepts the decision, the error symptom information is input into the decoder to generate a recovery operation and perform an error correction operation on the quantum state; (2) When the filter outputs a rejection decision, discard the current quantum state and trigger a protocol restart.

[0057] In this embodiment, after the protocol restart is triggered, the sender will restart a new round of quantum information encoding and transmission. Specifically, the sender will re-encode the quantum information to be transmitted according to the initially determined protocol parameters, forming a new logical quantum state, and retransmit it to the receiver through the quantum channel. The receiver will then re-perform the stabilization measurement to obtain new error symptom information, and input it into the filter again for acceptance or rejection. This process will continue until the filter outputs an acceptance decision, and the receiver successfully completes the error correction operation and obtains a reliable quantum state. This effectively avoids the impact of high-noise rounds on the reliability of quantum information transmission, ensuring that only rounds that have passed the filter and are determined to be low-noise will enter the subsequent error correction and application process. Thus, while ensuring the fidelity of the quantum state, the system's reliability and throughput are balanced through a dynamic round selection method.

[0058] Combination Figure 1 and Figure 2 As shown, at least one of the following methods can be used to implement the above step 110, where the filter makes an acceptance or rejection decision for the current round: In the first optional approach, based on the current protocol description, the decision rules corresponding to both the protocol description and the expected acceptance probability are obtained by establishing a correspondence between the protocol description and the decision rules. Based on the decision rules corresponding to the protocol description and expected acceptance probability, the reliability of the error symptom information is assessed, and the filter determines whether to accept or reject the current round. Thus, filtering is performed before error correction, and adjustments are made according to the acceptance probability, achieving a controllable trade-off between throughput and reliability, thereby addressing the problem of unadjustable accuracy and throughput.

[0059] The above decision rule is used to represent the preset noise threshold condition. For example, when the parameters characterizing the degree of noise interference to the quantum state (such as the number of error bits, phase flip probability, etc.) in the error symptom information obtained by the receiver through the stabilizer measurement are lower than or equal to the set threshold, the filter determines that the quantum state quality of the current round meets the requirements and outputs an acceptance decision; otherwise, if the parameter exceeds the threshold, it determines that the noise of the current round is too high and outputs a rejection decision.

[0060] In this embodiment of the application, the judgment method based on a clear threshold can quickly and objectively screen the quantum states in each round, ensuring that only quantum states that meet the preset quality standards will be used for subsequent error correction and information extraction, thereby reducing the problem of error correction failure or information distortion caused by high-noise rounds from the source.

[0061] Quantum network protocols in related technologies typically draw upon mature techniques from quantum information theory, primarily including error detection schemes and error correction schemes. Error detection schemes measure the encoded quantum state; when an error is detected, it is discarded and the protocol is re-executed. The advantage is its simplicity and lack of complex decoding requirements. However, its success probability decreases rapidly with increasing encoding scale or the number of measurement rounds. Error correction schemes actively correct errors through decoding algorithms, which can reduce the logical error rate to some extent. However, in the high-noise environment faced by quantum networks, relying solely on error correction often fails to achieve the reliability levels required for practical applications.

[0062] The related technologies generally regard error detection and error correction as independent or mutually exclusive technical approaches, lacking an error management framework that combines the advantages of both, which limits the ability of quantum networks to flexibly balance throughput and reliability.

[0063] To address the limitations of the aforementioned related technologies in flexibly balancing throughput and reliability in quantum networks, the error management method for quantum networks provided in this application combines traditional error detection and correction processes. Through the collaborative work of filters and decoders, it achieves the goal of dynamically adjusting throughput and reliability in quantum network protocol error management. See below for detailed explanation.

[0064] Figure 3 As shown Figure 1 The diagram shows a flowchart illustrating the internal structure of a filter in an error management method for quantum networks.

[0065] In the second alternative approach, step 111 involves quantifying and evaluating the current error symptom information according to a preset scoring function to obtain a corresponding score value. Thus, based on the error symptom information obtained during the execution of the quantum network protocol, the reliability of each transmission round is quantified and evaluated.

[0066] Based on the error symptom information obtained during the execution of the quantum network protocol, a two-stage error management structure consisting of a filter and a decoder is constructed. The transmission rounds are selectively accepted or rejected before decoding by scoring and scoring threshold decision method.

[0067] The preset scoring function used to calculate the score in the above filters can take different forms, including but not limited to functions based on statistical features of error symptom information, functions based on decoding cost differences, and functions based on the confidence level output by probabilistic models or machine learning models. The above-mentioned different implementation methods may differ in structural and algorithmic details, but they all have the following in common: they are all based on error symptom information and introduce an adjustable filtering decision method before error correction to achieve hierarchical control of the error management process, thereby achieving the same inventive purpose as the embodiments of this application, and all fall within the protection scope of the embodiments of this application, and will not be listed one by one here.

[0068] Step 112: Determine the corresponding scoring threshold based on the protocol description and expected acceptance probability.

[0069] Continue as Figure 3 The filter described above employs a scoring and scoring threshold decision structure based on error symptom information. After receiving error symptom information, the receiver first quantifies and evaluates the current error symptom information according to a preset scoring function to obtain a corresponding score value. Simultaneously, based on the protocol description and expected acceptance probability, a corresponding scoring threshold is determined. Subsequently, the score value is compared with the scoring threshold. When the score value meets preset decision conditions, an acceptance decision is output; when the score value does not meet the preset decision conditions, a rejection decision is output. In the above embodiments, the scoring threshold can be set or dynamically adjusted according to the expected acceptance probability or the protocol's expected throughput.

[0070] The aforementioned preset decision condition represents a specific comparison relationship between the score value and the score threshold. This relationship determines the filter's acceptance or rejection decision for the current transmission round. Specifically, when the score value is greater than or equal to the score threshold, the preset decision condition is satisfied, the filter outputs an acceptance decision, and the current quantum state is allowed to enter the subsequent decoding stage for error correction. When the score value is less than the score threshold, the preset decision condition is not satisfied, the filter outputs a rejection decision, the current quantum state is discarded, and the protocol restart process is triggered. Thus, by comparing the score value with the score threshold, the defined preset decision condition enables selective filtering by the filter, thereby improving the efficiency of error management and the overall performance of the quantum network protocol.

[0071] Step 113: Compare the score value with a scoring threshold. If the score value meets a preset decision condition, output an acceptance decision; if the score value does not meet the preset decision condition, output a rejection decision. Thus, a filtering method is introduced before the decoding operation, using the score and scoring threshold decision structure to determine whether to perform error correction for the current round. Furthermore, the acceptance probability is used as an explicitly adjustable system parameter, making the trade-off between throughput and reliability predictable and configurable.

[0072] In this embodiment, by introducing a filtering method based on scoring and a scoring threshold, the system acceptance probability becomes an explicitly adjustable parameter. By setting or dynamically calibrating the scoring threshold, a flexible and predictable trade-off can be achieved between throughput and reliability requirements for different application scenarios.

[0073] Specifically, the aforementioned scoring threshold is set or dynamically adjusted based on the expected acceptance probability or the protocol's desired throughput. The scoring threshold is calibrated by statistically analyzing the distribution of scoring values ​​during historical operation, ensuring that the filter meets the preset acceptance ratio requirements in long-term operation, thereby achieving an adjustable trade-off between transmission success rate and quantum state fidelity.

[0074] The aforementioned preset acceptance rate is the user's expected acceptance rate, ranging from 0% to 100%. The specific calibration scheme is as follows: The above decision rule is as follows: The receiver simulates the protocol multiple times locally. In each round, the score for that round is calculated by the scoring function. Then, all scores are ranked, and the lowest score of the top 20% or top 50% of the preset acceptance rates is taken as the scoring threshold.

[0075] The scoring thresholds in this specification can be determined in different ways, such as through offline calibration, online statistics, adaptive updates, or dynamic adjustment based on the expected throughput target.

[0076] In this embodiment, performance can be flexibly optimized in different application scenarios by dynamically adjusting the scoring threshold. For example, in precise quantum computing tasks with extremely high requirements for quantum state fidelity, the scoring threshold can be set to a higher level to filter out low-noise rounds, ensuring that the quantum states entering the decoding stage are more reliable, even if this may lead to a decrease in acceptance probability and an increase in the number of protocol restarts, thus sacrificing some throughput. In quantum key distribution scenarios with high real-time requirements, the scoring threshold can be appropriately lowered to increase the acceptance probability and reduce the latency caused by protocol restarts, prioritizing the continuity and efficiency of data transmission while meeting basic reliability requirements. Thus, based on the dynamic adjustment of the scoring threshold, the error management system of the quantum network can achieve a configurable balance between reliability and throughput according to actual application needs, improving the adaptability and practicality of the quantum network protocol in complex and ever-changing environments.

[0077] One relevant technology primarily employs quantum error correction for error management. In this approach, quantum information is first encoded onto multiple physical qubits. After quantum state transmission and manipulation, error symptom information is obtained by measuring the stable sub-operator. Subsequently, the decoder infers the most probable error based on the error symptom information and applies the corresponding recovery operation. Different quantum error-correcting codes have different adapted decoders; common decoders for surface codes include minimum-weighted matching decoders and machine learning-based decoders.

[0078] However, due to the complex sources of noise in quantum networks, not only do quantum states face high-intensity channel noise during transmission, but the encoding operations, stabilizer measurements, and decoding calculations involved in quantum error correction also introduce additional operational and measurement noise. Under the combined effect of these multi-source noises, if error management relies solely on error correction methods, the decoder's accuracy in inferring error symptoms will significantly decrease when the physical error rate is high, resulting in the corrected logical quantum state still having a high logical error rate. Under certain noise conditions, the additional errors introduced by quantum error correction encoding and decoding may even outweigh the error correction benefits, leading to situations where the logical error rate obtained after using an encoding system is higher than that obtained by direct transmission without encoding.

[0079] Compared to schemes that simply detect errors and discard them, this application's embodiment further performs error correction after the filter outputs an acceptance decision. This allows the accepted quantum state to maintain high fidelity while effectively repairing some correctable errors, thereby significantly improving the system's acceptance probability under the same reliability requirements. Thus, this application's embodiment can overcome the problem of the acceptance probability decreasing with noise levels in pure error detection schemes while ensuring quantum state quality, thereby improving the effective throughput of the quantum network. Details are as follows.

[0080] In a third alternative approach, the scoring function is constructed by the filter based on the number of checks triggered in the error symptom information.

[0081] Step 210: After completing the stable sub-measurement, the receiver obtains a set of error symptom information bits; each bit is used to indicate whether the corresponding check has been triggered.

[0082] Step 220: Determine the score value for the current round based on the number of checks triggered in the error symptom information using the filter.

[0083] Step 230: When the score value is higher than the score threshold, the filter outputs an acceptance decision; the acceptance decision is used to allow the decoder to generate a recovery operation based on the same error symptom information and correct the quantum state.

[0084] Step 240: When the score value is lower than the score threshold, the filter outputs a rejection decision; the rejection decision is used to discard the quantum state of the current round and trigger a protocol restart.

[0085] For example, in a quantum network protocol based on stable subcodes, stable submeasures generate a series of error symptom bits, each corresponding to whether a specific check has been triggered. The more checks are triggered, the greater the noise impact on the quantum state during transmission or processing, and the higher the score. When the score is higher than a preset score threshold, the filter determines that although the quantum state in the current round has some noise, it is still within an acceptable range, and thus outputs an acceptance decision, allowing the decoder to generate a recovery operation based on this error symptom information to correct the quantum state. Conversely, if the score is lower than the score threshold, it indicates that the quantum state noise is too large, and even with error correction, it is difficult to achieve the expected reliability. In this case, the filter outputs a rejection decision, directly discards the current quantum state, and triggers a protocol restart.

[0086] In this embodiment, the scoring function constructs a score value by statistically analyzing the number of triggered checks in the error symptom information. This method can intuitively reflect the degree of noise interference to the quantum state, thereby avoiding sending low-quality quantum states into subsequent processing and reducing ineffective error correction calculations and potential information distortion risks. In this way, a rapid quantitative assessment of quantum state quality is achieved, making filter decisions clear and operable.

[0087] Furthermore, the decoder is a least-weighted matching decoder; the step of determining the score value for the current round based on the number of triggered checks in the error symptom information through the filter includes: comparing the decoding costs generated under different logical assumptions through the filter, determining the difference between the decoding costs, and using this as the score value. When the score value is greater than the score threshold, it indicates that the logical state corresponding to the current error symptom information has a high confidence level, and the filter outputs an acceptance decision; otherwise, the filter outputs a rejection decision.

[0088] In one specific embodiment, the filter constructs a scoring function based on the number of triggered checks in the error symptom information. After completing the stable submeasurement, the receiver obtains a set of error symptom information bits; each bit indicates whether a corresponding check has been triggered. The filter counts the number of triggered checks in the error symptom information and calculates the score value for the current round based on this number. When the score value is higher than a scoring threshold, the filter outputs an acceptance decision, allowing the decoder to generate a recovery operation and correct the quantum state based on the same error symptom information; when the score value is lower than the scoring threshold, the filter outputs a rejection decision, the quantum state for the current round is discarded, and a protocol restart is triggered. This embodiment does not require calling the decoder's internal calculation process; it only relies on the statistical characteristics of the error symptom information to complete the decision, making it suitable for quantum network nodes with limited computing resources. Thus, the scoring function is the number of triggered checks in the error symptom information, making the calculation very simple—just counting the number.

[0089] The second related technology primarily employs error detection for error management. This type of scheme, after measuring error symptom information, if any measurement result indicates an error, directly discards the current quantum state and re-executes the protocol. This type of scheme is commonly used in quantum network protocols such as entanglement purification or entanglement distillation.

[0090] However, while pure error detection schemes can provide high quantum state fidelity in accepted rounds, their acceptance probability decreases rapidly with increasing noise intensity and number of measurements, severely limiting system throughput and hindering the practical deployment of large-scale quantum networks. Furthermore, in pure error detection schemes, whether a round is accepted is entirely determined by the measurement result, and the acceptance probability is jointly determined by the underlying physical noise and protocol structure, lacking explicit adjustment mechanisms. Therefore, it is difficult to finely control throughput according to system requirements. In practical quantum networks, different application scenarios often have explicit requirements for effective transmission rate and quality of service. Pure error detection schemes cannot guarantee quantum state reliability while providing predictable and configurable control over the acceptance probability, limiting their applicability in complex network environments.

[0091] Compared to quantum network error management schemes that rely solely on error correction, this application's embodiments introduce a filtering method based on error symptom information before decoding. The filter analyzes error information to determine which transmission rounds are more difficult to correct, thus eliminating those rounds. This effectively identifies and eliminates transmission rounds with high error correction difficulty under high-noise conditions, avoiding additional logical errors introduced by forcibly performing error correction operations when decoding is unreliable. Therefore, through this hybrid error management method, while maintaining a certain acceptance probability, the logical error rate of logical quantum states in accepted rounds can be significantly reduced, improving the overall stability of the system under high error rate conditions. Specifically: In a fourth alternative approach, the scoring function is constructed by the filter based on the decoder's processing results of the error symptom information.

[0092] Step 310: After obtaining the error symptom information, the receiver calls the decoder to process the error symptom information.

[0093] Step 320: Based on the decoding information output by the decoder during the generation of the recovery operation, the score value of the current round is determined by the filter; the score value of the current round is used to reflect the distinguishability or reliability level of the error symptom information under different logical assumptions. Step 330: When the score value is higher than the score threshold, the filter outputs an acceptance decision, and the decoder generates a recovery operation and performs an error correction operation based on the error symptom information.

[0094] Step 340: When the score value is lower than the score threshold, the filter outputs a rejection decision, the current quantum state is discarded, and the protocol is restarted.

[0095] Furthermore, the decoder is a neural network-based decoder; the neural network outputs a value that characterizes the probability of a logical error occurring or the prediction confidence level; the filter determines the score value based on the value output by the neural network.

[0096] In another specific embodiment, the filter constructs a scoring function based on the decoder's processing result of the error symptom information. After receiving the error symptom information, the receiver calls the decoder to process the error symptom information. The decoder generates a recovery operation and outputs decoding information, which may include, but is not limited to, cost information, probability information, or confidence information related to decoding reliability. The filter calculates the score value for the current round based on the information output by the decoder, reflecting the distinguishability or reliability level of the error symptom information under different logical assumptions. When the score value is higher than a scoring threshold, the filter outputs an acceptance decision, and the decoder generates a recovery operation and performs an error correction operation based on the error symptom information; when the score value is lower than the scoring threshold, the filter outputs a rejection decision, the current quantum state is discarded, and a protocol restart is triggered. Thus, the scoring function is slightly more complex and can further process the decoder's processing result. This also includes the following two sub-implementations.

[0097] As an optional embodiment of this application, the scoring function used in the filter to calculate the score value of the current round includes at least one of the following: a function based on the statistical characteristics of error symptom information, a function based on the difference in decoding costs, and a function based on the confidence level of the output of a probability model or a machine learning model.

[0098] In the first sub-implementation, the cost information described above is used with a minimum-weighted matching decoder. In this specific implementation, the decoder is a minimum-weighted matching decoder. The filter calculates the difference between the decoding costs generated under different logical assumptions as a score. When the cost difference is greater than a preset threshold, it indicates that the logical state corresponding to the current error symptom information has a high confidence level, and the filter outputs an acceptance decision; otherwise, the filter outputs a rejection decision. Thus, the decoder provides the decoding result, as well as the "cost information" for different decoding results. The filter calculates the difference in costs between different decoding results, and only accepts the result when the cost difference is greater than a threshold.

[0099] In the second sub-implementation, a neural network decoder is used to utilize the aforementioned confidence information. In this specific implementation, the decoder is a neural network-based decoder, and the neural network outputs a numerical value representing the confidence level of a predicted logical error. The filter calculates a score based on the output value and compares it with a scoring threshold to determine whether to accept the transmission result for the current round. Thus, the neural network decoder provides a decoding result and its confidence level. The filter performs simple processing on this confidence level (using a logit function), accepting the result if it is greater than the scoring threshold.

[0100] In this embodiment, a hybrid error management approach is used to significantly reduce the logic error rate of the accepted quantum state while maintaining a controllable throughput, resulting in superior overall performance compared to pure error correction or pure error detection schemes.

[0101] The scoring threshold settings described above allow for flexible trade-offs. For example, users can preset the acceptance ratio (which, in specific applications such as entangled pair distribution, can be understood as throughput) to determine the corresponding scoring threshold and set the filter's scoring threshold to this value. Users can set different acceptance ratios / throughput rates to flexibly balance throughput and reliability.

[0102] Compared to a pure calibration approach, the embodiments of this application improve accuracy; Compared to pure detection solutions, the embodiments of this application improve throughput; In summary, the embodiments of this application achieve a flexible trade-off between throughput and accuracy.

[0103] This application embodiment is applied to application scenarios such as quantum networks where there are clear constraints on service quality and resource utilization, overcoming the problem of uncontrollable acceptance probability in pure error detection schemes of related technologies.

[0104] This application addresses the challenge of balancing throughput and reliability in error management within quantum networks by introducing a hybrid error management approach. This approach ensures quantum state quality while allowing for controllable adjustment of the acceptance probability, thereby improving the overall performance of quantum networks in complex noisy environments. Similar effects can be achieved from other perspectives, such as employing different quantum error correction codes or different quantum network protocols.

[0105] In this application embodiment, a specific scoring function, threshold calculation method, or decoder implementation, by introducing a filtering decision method based on error symptom information into the error correction process, achieves system-level optimization of the error management process of quantum networks.

[0106] Figure 4 The diagram shown is a structural schematic of the electronic device 50 provided in an embodiment of this application.

[0107] like Figure 4 As shown, the electronic device 50 includes one or more processors 51 for implementing the error management method for quantum networks as described above.

[0108] In some embodiments, electronic device 50 may include storage medium 59. For example, computer-readable storage medium may store a program that can be invoked by processor 51, and may include non-volatile storage medium. In some embodiments, electronic device 50 may include memory 58 and interface 57. In some embodiments, electronic device 50 may also include other hardware depending on the specific application.

[0109] The electronic devices described herein can be: desktop computers, portable computers, smart mobile terminals, servers, PDAs (Personal Digital Assistants), and handheld terminals, etc. Among them, PDAs can include industrial PDAs and consumer PDAs. Any electronic device that can implement the embodiments of this invention is within the scope of protection of this invention and is not limited herein.

[0110] The computer-readable storage medium of this application embodiment stores a program that, when executed by processor 51, is used to implement the error management method for quantum networks as described above.

[0111] This application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described in any of the preceding claims.

[0112] This application also provides a computer program stored in a computer-readable storage medium, for example... Figure 4 The storage medium 59, and when the processor executes the computer program, causes the processor 51 to perform the method described above.

[0113] This application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented using any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0114] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0115] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element qualified by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. An error management method for quantum networks, characterized in that, include: In each execution round of the quantum network protocol, the receiver performs a reliability assessment on the error symptom information obtained from the measurement, and the filter determines whether to accept or reject the current round. The filter is used to filter out high-noise cycles; When the filter output accepts a decision, the decoder generates a corresponding recovery operation based on the same error symptom information and performs an error correction operation on the quantum state. When the filter outputs a rejection decision, the current quantum state is discarded and the protocol is restarted.

2. The error management method for quantum networks as described in claim 1, characterized in that, The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: Based on the current protocol description, the decision rules corresponding to both the protocol description and the expected acceptance probability are established with the decision rules to obtain the corresponding decision rules. Based on the decision rules corresponding to the protocol description and the expected acceptance probability, the reliability of the error symptom information is assessed, and the filter determines whether to accept or reject the current round.

3. The error management method for quantum networks as described in claim 1 or 2, characterized in that, The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: The current error symptom information is quantitatively evaluated based on a preset scoring function to obtain the corresponding score value; Determine the corresponding scoring threshold based on the agreement description and the expected acceptance probability; The score is compared with a score threshold, and when the score meets a preset decision condition, an acceptance decision is output. When the score does not meet the preset decision conditions, a rejection decision is output.

4. The error management method for quantum networks as described in claim 3, characterized in that, The scoring threshold is set or dynamically adjusted based on the expected acceptance probability or the protocol's expected throughput.

5. The error management method for quantum networks as described in claim 3, characterized in that, The scoring function is constructed by the filter based on the number of checks triggered in the error symptom information; The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: After completing the stabilization sub-measurement, a set of error symptom information bits is obtained; each bit is used to indicate whether the corresponding check has been triggered. The filter determines the score for the current round based on the number of checks triggered in the error symptom information. When the score value is higher than the score threshold, the filter outputs an acceptance decision; the acceptance decision is used to allow the decoder to generate a recovery operation and correct the quantum state based on the same error symptom information; When the score value is lower than the score threshold, the filter outputs a rejection decision; the rejection decision is used to discard the quantum state of the current round and trigger a protocol restart.

6. The error management method for quantum networks as described in claim 5, characterized in that, The decoder is a minimum weight matching decoder; The step of determining the score for the current round based on the number of triggered checks in the error symptom information through the filter includes: The filter compares the decoding costs generated under different logical assumptions to determine the differences between the decoding costs, which are then used as a score.

7. The error management method for quantum networks as described in claim 3, characterized in that, The scoring function is constructed by the filter based on the decoder's processing results of the error symptom information; The receiver performs a reliability assessment on the error symptom information obtained from measurement, and the filter makes an acceptance or rejection decision for the current round, including: After obtaining the error symptom information, the decoder is invoked to process the error symptom information; The score for the current round is determined by a filter based on the decoding information output by the decoder during the generation of the recovery operation; the score for the current round is used to reflect the distinguishability or reliability level of the error symptom information under different logical assumptions. When the score value is higher than the score threshold, the filter outputs an acceptance decision, and the decoder generates a recovery operation and performs an error correction operation based on the error symptom information. When the score value is lower than the score threshold, the filter outputs a rejection decision, the current quantum state is discarded, and the protocol is restarted.

8. The error management method for quantum networks as described in claim 7, characterized in that, The decoder is a neural network-based decoder; the neural network outputs a value used to characterize the probability of a logical error occurring or the prediction confidence level. The score is determined by the filter based on the value output by the neural network.

9. The error management method for quantum networks as described in any one of claims 4 to 8, characterized in that, The scoring function used in the filter to calculate the score value of the current round includes at least one of the following: a function based on statistical features of error symptom information, a function based on decoding cost differences, and a function based on the output confidence of a probabilistic model or machine learning model.

10. An electronic device, characterized in that, It includes one or more processors for implementing the error management method for quantum networks as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the error management method for quantum networks as described in any one of claims 1 to 9.

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