A quantum transmission error correction method, device, equipment, medium and product

Abstract

This application provides a quantum transmission error correction method, apparatus, device, medium, and product, relating to the field of quantum mechanics. The method inputs real-time quantum noise data acquired during quantum transmission into a first error correction model to obtain a first error correction strategy. Based on this strategy, errors in quantum transmission are corrected, and a first communication quality index of the quantum transmission link before and after correction is obtained. The reward value of the first error correction strategy is then determined. Based on this reward value, the Q-value function of the error correction model is adjusted to obtain a second error correction model, thus updating the strategy. This solution utilizes a reinforcement learning algorithm to adaptively adjust the model based on real-time correction feedback. This not only enables the error correction model to adapt to changes in the noise environment but also provides effective error correction strategies to address noise-related problems, thereby improving the reliability, stability, and data processing quality of the quantum transmission link, as well as enhancing the performance of the execution device in complex noise environments.

Description

Technical Field

[0001] This application belongs to the field of quantum mechanics, and in particular relates to a quantum transmission error correction method, device, equipment, medium and product. Background Technology

[0002] Quantum computing and quantum communication are emerging technologies based on the principles of quantum mechanics, both requiring quantum transmission. During quantum transmission, data error correction primarily relies on static methods. Common quantum error correction methods include surface code error correction and distortion code error correction, which can correct noise-induced errors by adding redundant qubits. However, these traditional methods lack dynamic adaptability and cannot dynamically adjust to changes in noise; that is, they cannot adjust the quantum data according to real-time changes in the noise environment during quantum transmission, resulting in lower performance of the execution device in complex noisy environments. Summary of the Invention

[0003] This application provides a quantum transmission error correction method, apparatus, device, medium, and product, which can adjust quantum data according to real-time changes in the noise environment during quantum transmission, thereby improving the performance of the execution device.

[0004] On one hand, embodiments of this application provide a quantum transmission error correction method, including:

[0005] Acquire quantum noise data in a quantum transmission link;

[0006] The quantum noise data is input into the first error correction model to obtain the corresponding first error correction strategy; the first error correction model is trained based on a reinforcement learning model.

[0007] Based on the first error correction strategy, the errors in the quantum transmission link are corrected to obtain the current corrected quantum transmission link.

[0008] Obtain the first communication quality index of the quantum transmission link after the current correction;

[0009] Based on the first communication quality index, determine the reward value of the first error correction strategy;

[0010] Based on the reward value, the Q-value function in the first error correction model is adjusted to obtain the second error correction model;

[0011] Update the first error correction model to the second error correction model, and return to the step of obtaining quantum noise data in the quantum transmission link.

[0012] As some optional embodiments, the reward value of the first error correction strategy is determined based on the first communication quality index, including:

[0013] The reward value is determined based on the magnitude of the first communication quality index. When the model adjustment objective is to improve the first communication quality index, the reward value is positively correlated with the first communication quality index. When the model adjustment objective is to reduce the first communication quality index, the reward value is negatively correlated with the first communication quality index.

[0014] As some optional embodiments, the first communication quality metric includes quantum fidelity;

[0015] Correspondingly, based on the first communication quality indicator, the reward value of the first error correction strategy is determined, including:

[0016] The reward value is determined based on the magnitude of the quantum fidelity; the reward value is positively correlated with the quantum fidelity.

[0017] As some optional embodiments, the first communication quality indicator includes the data transmission error rate;

[0018] Correspondingly, based on the first communication quality indicator, the reward value of the first error correction strategy is determined, including:

[0019] The reward value is determined based on the magnitude of the data transmission error rate; the reward value is negatively correlated with the data transmission error rate.

[0020] As some optional embodiments, based on the first error correction strategy, errors in the quantum transmission link are corrected, including any one or any combination of the following:

[0021] Determine the target error correction code to be corrected; the target error correction code includes a surface code and a distortion code;

[0022] Adjust the order of quantum gate operations;

[0023] Adjust the period of quantum gate operations.

[0024] As some optional embodiments, based on the first error correction strategy, errors in the quantum transmission link are corrected to obtain the current corrected quantum transmission link, including:

[0025] Based on the first error correction strategy, determine the corresponding error correction operation;

[0026] Perform the error correction operation;

[0027] Obtain the second communication quality index after performing the error correction operation;

[0028] If the second communication quality index does not meet the preset conditions, the error correction operation is updated based on the second communication quality index and the first error correction strategy, and the step of executing the error correction operation is returned until the second communication quality index meets the preset conditions, so as to obtain the current corrected quantum transmission link.

[0029] As some optional embodiments, based on the reward value, the Q-value function in the first error correction model is adjusted to obtain a second error correction model, including:

[0030] Obtain the current Q-value, preset discount factor, and preset learning rate of the first error correction strategy;

[0031] Based on the preset discount factor, the preset learning rate, the reward value, and the first Q value, determine the second Q value corresponding to the first error correction strategy;

[0032] The second error correction model is determined based on the second Q value.

[0033] As some optional embodiments, acquiring quantum noise data in a quantum transmission link includes:

[0034] Obtain raw quantum noise data;

[0035] The quantum noise data is obtained by extracting the feature data from the original quantum noise data; the feature data includes any one or any combination of the following: decoherence time data, quantum gate operation error data, and bit crosstalk data.

[0036] As some optional embodiments, acquiring quantum noise data in a quantum transmission link includes:

[0037] The quantum noise data is obtained based on a quantum noise monitoring model;

[0038] Before acquiring quantum noise data in the quantum transmission link, the method further includes:

[0039] Determine the quantum hardware platform to perform the quantum teleportation;

[0040] The quantum noise monitoring model and the first error correction model are adjusted based on the type of the quantum hardware platform.

[0041] On the other hand, this application also provides a quantum transmission error correction device, the device comprising:

[0042] The first acquisition module is used to acquire quantum noise data in the quantum transmission link;

[0043] The input module is used to input the quantum noise data into the first error correction model to obtain the corresponding first error correction strategy; the first error correction model is trained based on a reinforcement learning model.

[0044] The correction module is used to correct errors in the quantum transmission link based on the first error correction strategy, so as to obtain the quantum transmission link after the current correction.

[0045] The second acquisition module is used to acquire the first communication quality index of the quantum transmission link after the current correction.

[0046] The determination module is used to determine the reward value of the first error correction strategy based on the first communication quality index;

[0047] An adjustment module is used to adjust the Q-value function in the first error correction model based on the reward value to obtain a second error correction model;

[0048] An update module is used to update the first error correction model to a second error correction model and return to the step of obtaining quantum noise data in the quantum transmission link.

[0049] On the other hand, this application also provides a quantum transmission error correction device, the device comprising: a processor and a memory storing computer program instructions;

[0050] When the processor executes the computer program instructions, it implements the quantum transmission error correction method as described above.

[0051] On the other hand, this application also provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the quantum transmission error correction method as described above.

[0052] On the other hand, this application also provides a computer program product in which the instructions, when executed by the processor of an electronic device, cause the electronic device to perform the quantum transmission error correction method as described above.

[0053] The quantum transmission error correction method of this application embodiment pre-trains a first error correction model based on a reinforcement learning model. Then, it inputs real-time quantum noise data acquired during quantum transmission into the first error correction model to obtain a first error correction strategy. Based on this strategy, errors in quantum transmission are corrected, and a first communication quality index of the quantum transmission link before and after correction is obtained. The reward value of the first error correction strategy is then determined. Based on this reward value, the Q-value function of the error correction model is adjusted to obtain a second error correction model, which updates the error correction strategy in real time and performs link error correction again. Since the error correction model in this application can adjust its Q-value function based on real-time feedback data, the adjusted second error correction model can output a second error correction strategy that better meets the requirements based on actual feedback. In other words, this solution, through reinforcement learning algorithms, can adaptively adjust the model based on real-time correction feedback. This not only enables the error correction model to adapt to changes in the noise environment but also provides effective error correction strategies to solve problems caused by noise, thereby improving the reliability, stability, and data processing quality of the quantum transmission link, as well as improving the performance of the execution device in complex noise environments. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This paper illustrates a flowchart of a quantum transmission error correction method provided in an embodiment of this application.

[0056] Figure 2 A flowchart illustrating a specific implementation of S103 provided in an embodiment of this application is shown;

[0057] Figure 3 A flowchart illustrating a specific implementation of S106 provided in an embodiment of this application is shown;

[0058] Figure 4 A flowchart illustrating a specific implementation of S101 provided in an embodiment of this application is shown;

[0059] Figure 5 A schematic diagram of the structure of the quantum communication system provided in an embodiment of this application is shown;

[0060] Figure 6 This is a schematic diagram of a quantum transmission error correction device provided in an embodiment of this application;

[0061] Figure 7 A schematic diagram of a quantum transmission error correction device provided in an embodiment of this application is shown. Detailed Implementation

[0062] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0064] Quantum computing and quantum communication are emerging technologies based on the principles of quantum mechanics, both involving the transmission of quantum data. During quantum transmission, error correction primarily relies on static methods. Common quantum error correction methods include surface code error correction and distortion code error correction, which can correct noise-induced errors by adding redundant qubits. However, these traditional methods lack dynamic adaptability and cannot dynamically adjust to changes in noise; that is, they cannot adjust the quantum data according to real-time changes in the noise environment during quantum transmission, resulting in lower performance of the execution device in complex noisy environments.

[0065] To address the shortcomings of traditional error correction methods in terms of dynamic adaptability, this application proposes a novel error correction method for quantum transmission, which combines a reinforcement learning model to achieve dynamic error correction in quantum transmission. Specifically, after the error correction model outputs and executes an error correction policy, the model is dynamically adjusted in real time based on the effectiveness of the currently executed policy. The adjusted model can then output a new error correction policy that is more suitable for the current real-world environment.

[0066] Based on this, embodiments of this application provide a quantum transmission error correction method, apparatus, device, computer storage medium, and computer program product. The quantum transmission error correction method provided in the embodiments of this application will be described first below. Figure 1 A schematic flowchart of a quantum transmission error correction method provided in an embodiment of this application is shown. Figure 1 As shown, the method includes the following steps: S101 to S107.

[0067] S101: Acquire quantum noise data in the quantum transmission link.

[0068] Quantum noise data refers to measurement data in a quantum system that is affected by various noise sources. This noise can adversely affect the processing, storage, and transmission of quantum information.

[0069] This application monitors the noise state in the quantum transmission link in real time, collecting data on noise characteristics such as qubit decoherence time, quantum gate operation errors, and crosstalk between qubits through sensors or quantum detectors. The monitored quantum noise data will be used to generate subsequent error correction strategies. Qubit decoherence time is typically only present in quantum communication.

[0070] S102: Input the quantum noise data into the first error correction model to obtain the corresponding first error correction strategy.

[0071] The first error correction model is trained based on a reinforcement learning model. The first error correction model mentioned in this application can be a model trained in advance in a virtual environment, or a model trained based on data generated during actual quantum transmission.

[0072] The quantum noise data obtained in the aforementioned steps is input into the first error correction model to obtain the first error correction strategy output by the model. It should be noted that the specific content of the error correction strategy mentioned in this application is not limited and can be selected according to the actual situation. As some optional embodiments, the first error correction strategy may be to determine the target error correction code to be corrected, such as surface codes and distortion codes; it may also be to adjust the order of quantum gate operations or adjust the period of quantum gate operations, etc.

[0073] S103: Based on the first error correction strategy, the errors in the quantum transmission link are corrected to obtain the current corrected quantum transmission link.

[0074] Typically, the error correction strategy is not a specific error correction operation performed on the quantum transmission link, but a high-level strategy. Based on this high-level strategy, specific error correction operations are then performed according to the actual situation.

[0075] For example, in some feasible embodiments, the first error correction strategy may be any one or any combination of determining the target error correction code to be corrected, adjusting the order of quantum gate operations, or adjusting the period of quantum gate operations. Assuming the determined first error correction strategy is to adjust the order of quantum gate operations, then when performing a specific error correction operation, a specific adjustment scheme for the order of quantum gate operations will be determined.

[0076] In practical applications, the initial error correction operation based on the first error correction strategy may not achieve satisfactory results. Therefore, after performing the error correction operation, the specific error correction operation can be repeatedly adjusted based on the subsequent performance to ensure the effectiveness of the error correction. The effectiveness of the error correction operation can be determined based on the communication quality indicators of the quantum transmission link.

[0077] S104: Obtain the first communication quality index of the quantum transmission link after the current correction.

[0078] After correcting errors in the quantum transmission link based on the first error correction strategy to obtain the corrected quantum transmission link, it is necessary to obtain the first communication quality index of the corrected quantum transmission link. As some optional embodiments, the first communication quality index may include the quantum fidelity of the quantum transmission link and the data transmission error rate, etc.

[0079] S105: Determine the reward value of the first error correction strategy based on the first communication quality index.

[0080] As mentioned above, the first communication quality index may include the quantum fidelity of the quantum transmission link and the data transmission error rate, etc.; based on the first communication quality index, the reward value corresponding to the first error correction strategy can be determined. As mentioned above, the first error correction model of this application is obtained by training a reinforcement learning model, so the reward value of the first error correction strategy represents the effect of executing the first error correction strategy in the current noisy environment.

[0081] Specifically, assuming the first communication quality indicator includes quantum fidelity, the reward value is positively correlated with the magnitude of quantum fidelity; if the first communication quality indicator includes data transmission error rate, the reward value is negatively correlated with the magnitude of data transmission error rate.

[0082] S106: Based on the reward value, adjust the Q-value function in the first error correction model to obtain the second error correction model.

[0083] In this application, the Q-value function in the first error correction model is adjusted using the reward value of the first error correction strategy to obtain the second error correction model. When the same noisy environment occurs again, the second error correction model will select the corresponding error correction strategy based on the new Q-value function. The new error correction strategy enables the communication quality indicators of the quantum transmission link to better meet practical needs, such as improving the quantum fidelity of the quantum transmission link or reducing the data transmission error rate of the quantum transmission link.

[0084] S107: Update the first error correction model to the second error correction model and return to S101.

[0085] Reinforcement learning models, through continuous interaction with the environment, allow an agent to gradually learn which actions and states yield higher rewards, thereby adjusting its strategy to maximize accumulated rewards over the long term. Therefore, after adjusting the first error correction model based on feedback data from the first error correction strategy to obtain the second error correction model, it is necessary to update the first error correction model to the second error correction model and repeatedly execute the above steps by re-acquiring quantum noise data from the quantum transmission link. This achieves dynamic adjustment of the error correction model, and consequently, dynamic adjustment of the error correction strategy.

[0086] As can be seen, this application constructs a complete real-time feedback mechanism by introducing reinforcement learning algorithms. This mechanism can dynamically adjust the error correction strategy based on the actual noise conditions and communication results in the quantum transmission link, thereby achieving continuous optimization and overcoming the shortcomings of insufficient feedback mechanisms in traditional schemes. Furthermore, this application realizes dynamic adjustment of the error correction strategy based on real-time noise states, which can effectively solve the limitations of static error correction strategies in traditional schemes and ensure that quantum communication maintains efficient error correction in complex and variable noise environments.

[0087] Meanwhile, this application leverages the global optimization capabilities of reinforcement learning to cover the entire quantum transmission process, including data transmission, noise management, and error correction strategy execution, effectively addressing the problem of single optimization objectives in traditional schemes. Furthermore, this application utilizes the intelligent resource allocation capabilities of reinforcement learning models to reduce redundant resource waste in high-noise environments, while simultaneously optimizing the allocation of computational and bandwidth resources in quantum communication, significantly improving system resource utilization. The proposed method is applicable to multiple scenarios and various types of noisy environments, overcoming the limitations of traditional schemes. Whether in quantum key distribution or broader quantum communication networks, this application demonstrates both high efficiency and adaptability.

[0088] The quantum transmission error correction method of this application embodiment pre-trains a first error correction model based on a reinforcement learning model. Then, it inputs real-time quantum noise data acquired during quantum transmission into the first error correction model to obtain a first error correction strategy. Based on this strategy, errors in quantum transmission are corrected, and a first communication quality index of the quantum transmission link before and after correction is obtained. The reward value of the first error correction strategy is then determined. Based on this reward value, the Q-value function of the error correction model is adjusted to obtain a second error correction model, which updates the error correction strategy in real time and performs link error correction again. Since the error correction model in this application can adjust its Q-value function based on real-time feedback data, the adjusted second error correction model can output a second error correction strategy that better meets the requirements based on actual feedback. In other words, this solution, through reinforcement learning algorithms, can adaptively adjust the model based on real-time correction feedback. This not only enables the error correction model to adapt to changes in the noise environment but also provides effective error correction strategies to solve problems caused by noise, thereby improving the reliability, stability, and data processing quality of the quantum transmission link, as well as improving the performance of the execution device in complex noise environments.

[0089] In practical applications, the correspondence between different first communication quality indicators and reward values ​​varies. Therefore, in order to determine the reward value more accurately, this application provides a specific implementation method.

[0090] Specifically, the reward value can be determined based on the magnitude of the first communication quality index. When the model adjustment objective is to improve the first communication quality index, the reward value is positively correlated with the first communication quality index; when the model adjustment objective is to decrease the first communication quality index, the reward value is negatively correlated with the first communication quality index. Some specific examples are provided below.

[0091] In one optional embodiment, the first communication quality metric includes quantum fidelity; quantum fidelity is an important concept in the field of quantum information used to measure the similarity between two quantum states. In quantum error correction, it is necessary to determine the similarity between the quantum state after noise interference and the original quantum state, and quantum fidelity can serve as an indicator of the error correction effect. If the fidelity is high, it indicates that the error correction process has successfully recovered most of the information of the original quantum state.

[0092] Correspondingly, in this embodiment, determining the reward value of the first error correction strategy based on the first communication quality index includes: determining the magnitude of the reward value based on the magnitude of quantum fidelity. Since the goal of the error correction strategy is to improve the quantum fidelity in the quantum transmission link, the reward value is positively correlated with the quantum fidelity.

[0093] In this implementation, the quantum fidelity of the quantum transmission link is used as a reference indicator for error correction effectiveness. Based on the magnitude of the quantum fidelity, a reward value positively correlated with it is determined. Then, the Q-value of the error correction model is adjusted based on the reward value, enabling the error correction strategy output by the model to improve the quantum fidelity of the transmission link. This, in turn, ensures the reliability, stability, and data processing quality of quantum transmission, and improves the performance of the execution device in complex noisy environments.

[0094] In one alternative embodiment, the first communication quality metric includes the data transmission error rate; the quantum data transmission error rate is a key indicator for measuring the performance of a quantum communication system, referring to the proportion of erroneous data occurring during quantum data transmission.

[0095] Correspondingly, in this embodiment, determining the reward value of the first error correction strategy based on the first communication quality index includes: determining the reward value based on the magnitude of the data transmission error rate; the reward value is negatively correlated with the data transmission error rate. Since the correction goal of the error correction strategy is to reduce the data transmission error rate in the quantum transmission link, the reward value is negatively correlated with the data transmission error rate.

[0096] In this implementation, the data transmission error rate in quantum transmission is used as a reference indicator for the error correction effect. Based on the magnitude of the data transmission error rate, a reward value that is negatively correlated with it is determined. Then, the Q value of the error correction model is adjusted based on the reward value, so that the error correction strategy output by the error correction model can reduce the data transmission error rate of the transmission link.

[0097] In this implementation, when the model adjustment objective is to improve the first communication quality index, the reward value is positively correlated with the first communication quality index; when the model adjustment objective is to decrease the first communication quality index, the reward value is negatively correlated with the first communication quality index. Therefore, by accumulating the reward value through the Q-value, the error correction model will ultimately prioritize the strategy that meets the requirements (improving or decreasing the first communication quality index), thus achieving the model adjustment objective.

[0098] A suitable error correction strategy can ensure better error correction results; therefore, this embodiment provides a feasible implementation method. Based on the first error correction strategy, errors in the quantum transmission link are corrected, including any one or any combination of the following:

[0099] Determine the target error correction code to be corrected; the target error correction code includes the surface code and the distortion code;

[0100] Adjust the order of quantum gate operations;

[0101] Adjust the period of quantum gate operations.

[0102] In practical applications, a suitable target error correction code can be selected from the surface code and the distorted code, or any one or more error correction strategies can be selected, such as adjusting the order of quantum gate operations and adjusting the period of quantum gate operations.

[0103] In this embodiment, by selecting an appropriate strategy for the correction operation, the error correction effect can be guaranteed. For example, strategies such as selecting the target error correction code, adjusting the order and period of quantum gate operations can be chosen to obtain the optimal strategy that meets the current requirements. Then, based on the selected first error correction strategy, a suitable correction operation can be further selected, thereby ensuring the error correction effect.

[0104] To achieve better error correction, this application also provides a specific implementation of S103. Figure 2 A flowchart illustrating a specific implementation of S103 provided in an embodiment of this application is shown. Figure 2 As shown, S103 can specifically include: S1031 to S1035.

[0105] S1031: Based on the first error correction strategy, determine the corresponding error correction operation.

[0106] S1032: Perform error correction operation.

[0107] It is evident that selecting the first error correction strategy that best meets the current requirements only yields a high-level strategy. This first error correction strategy is not the specific error correction operation performed on the quantum transmission link; rather, it requires further determination of the specific error correction operation based on the first error correction strategy. For example, if the selected target error correction code is a surface code, the specific correction is performed based on the surface code.

[0108] S1033: Obtain the second communication quality indicator after performing the error correction operation.

[0109] After performing the error correction operation, it is necessary to obtain the second communication quality index of the quantum transmission link after the error correction operation. As some optional implementation methods, the second communication quality index may include the quantum fidelity of the quantum transmission link and the data transmission error rate.

[0110] S1034: Determine whether the second communication quality index meets the preset conditions; if the second communication quality index does not meet the preset conditions, execute S1035; if the second communication quality index meets the preset conditions, obtain the current corrected quantum transmission link and execute S104.

[0111] S1035: Based on the second communication quality index and the first error correction strategy, update the error correction operation and return to S1032.

[0112] It is evident that, given the current unsatisfactory error correction performance, the error correction operation can be updated based on the first error correction strategy. That is, if the first error correction strategy remains unchanged, assuming it is based on a target error correction code, and the selected target error correction code is specifically a surface code, then the updated error correction operation will still be based on the surface code; only the specific method of error correction based on the surface code differs between the previous and updated operations. After multiple error correction updates, and provided the second communication quality index meets preset conditions, the current corrected quantum transmission link is obtained.

[0113] In this application, the specific content of the preset conditions is not limited and is determined according to the actual situation. Generally, the second communication quality index can be determined to meet the preset conditions when, after the current error correction operation, the second communication quality index no longer changes towards the required adjustment target, or the degree of change of the second communication quality index is less than the threshold.

[0114] In this embodiment, based on the first error correction strategy, different error correction operations are determined multiple times to correct errors in the quantum transmission link. Based on the effect of each error correction operation, the error correction operation is updated and the next error correction is executed. This aims to optimize the error correction effect under the current first error correction strategy.

[0115] In order to better adjust the model and output an effective error correction strategy, this application provides a specific method for adjusting the Q-value function based on the reward value. Figure 3 A flowchart illustrating a specific implementation of S106 provided in an embodiment of this application is shown. Figure 3 As shown, S106 may include S1061 to S1063.

[0116] S1061: Obtain the current Q-value, preset discount factor, and preset learning rate of the first error correction policy.

[0117] S1062: Determine the second Q value corresponding to the first error correction strategy based on the preset discount factor, preset learning rate, reward value, and first Q value.

[0118] Specifically, the Q-value update formula for reinforcement learning can be shown in formula (1), and the model continuously adjusts the Q-value through this formula.

[0119] Q(s t ,a t )=Q(s t ,a t )+α(r t +γmax a Q(s t+1 ,a)-Q(s t ,a t )) (1)

[0120] Where Q(s) t ,a t ) represents the current state s t Next, execute action a t Q value; r t γ is the immediate reward value, γ is the discount factor, and α is the learning rate.

[0121] max a Q(s t+1 a) is the next state s t+1 At that time, the maximum Q value obtainable by taking all possible actions 'a' can be understood as, if state s is reached... t+1 And then, if we follow the current optimal strategy, what is the expected cumulative reward in the future? This is an estimate of the best future value.

[0122] r t +γmax a Q(s t+1 a) is based on the immediate reward r we currently receive. t and the maximum estimate of the best future value a Q(s t+1 a), the determined pair Q(s) t ,a t This should be a new estimate. It combines the actual returns of the current step with the expected returns for the future.

[0123] r t +γmax a Q(s t+1 ,a)-Q(s t a t This difference is the Temporal Difference Error (TD error). It represents the gap between the new target value and the current estimate, and this error drives the learning and adjustment of the Q value.

[0124] S1063: Determine the second error correction model based on the second Q value.

[0125] Once the updated second Q value is obtained, the second error correction model can be determined.

[0126] This embodiment provides a specific method for training a Q-value model. By updating the Q-value using a reward value, a discount factor, and a learning rate, the target Q-value of the first error correction strategy can be obtained, thereby efficiently determining the second error correction model. Based on the new Q-value, the second error correction model can output a more effective error correction strategy.

[0127] In this embodiment, the noise state in quantum communication can be monitored in real time based on a quantum noise monitoring model, capturing various noise sources (such as decoherence of qubits, quantum gate operation errors, bit crosstalk, etc.). These noise features will serve as input data for a reinforcement learning model, supporting subsequent error correction strategy optimization. A specific method for acquiring quantum noise data is provided here. Figure 4 A flowchart illustrating a specific implementation of S101 provided in an embodiment of this application is shown. Figure 4 As shown, S101 may include S1011 and S1012.

[0128] S1011: Obtain raw quantum noise data.

[0129] In practical applications, noise data of qubits can be collected in real time using quantum detectors (such as microwave detectors and laser detectors).

[0130] S1012: Extract feature data from the original quantum noise data to obtain quantum noise data.

[0131] The characteristic data includes any one or any combination of the following: decoherence time data, quantum gate operation error data, and bit crosstalk data. Decoherence time is the time it takes for a quantum bit to lose quantum information, usually represented by T1 (horizontal) and T2 (vertical). Quantum gate operation error is the error introduced during quantum gate operation, usually expressed as the execution error E of the quantum gate. gate Bit crosstalk is the effect of the state of one qubit on the states of other qubits. The basic form of noise data acquisition can be expressed by the formula:

[0132] Noise data = f(T1,T2,E) gate (2) Crosstalk

[0133] Where T1 and T2 are the decoherence times of the qubit, respectively; E gate This refers to the execution error of a quantum gate; crosstalk refers to the influence of the state of one quantum bit on other quantum bits.

[0134] In practical applications, noisy data can be processed through denoising and standardization to obtain data that meets the model's requirements. Since the original data may contain unnecessary high-frequency noise or outliers, techniques such as wavelet transform or low-pass filtering can be used to denoise it, ensuring data purity. After extracting features to obtain quantum noise data, the quantum noise data can be standardized to ensure the consistency of the input data. A common standardization method is to scale the data to a uniform range (e.g., [0,1]) to prevent certain noise features from having an excessive impact on the learning process. Finally, the preprocessed noisy data is input into the reinforcement learning model as the model's state input.

[0135] Assume the monitored noise data are x1, x2, ..., x n The normalization formula for noise data is:

[0136]

[0137] Where x represents the noise data, μ is the mean of the data, and σ is the standard deviation of the data. norm It is normalized noise data.

[0138] In this implementation, the raw quantum noise data is first acquired, and then features such as decoherence time data, quantum gate operation error data, and bit crosstalk data are extracted from the data. Finally, the required quantum noise data is quickly obtained. Furthermore, in practical applications, the raw quantum noise data can be denoised first, and the data can be standardized after feature extraction. The standardized data can then be input into the model, which improves the model's data processing efficiency and achieves better error correction.

[0139] In practice, different quantum hardware platforms require different parameter adaptations for the model. To better achieve the desired model performance, a specific approach is provided here. In practical applications, quantum noise data can be acquired based on a quantum noise monitoring model; this noise monitoring model is then used to output the corresponding error correction strategy based on the quantum noise data. Therefore, before acquiring the quantum noise data in the quantum transmission link, the quantum hardware platform performing the quantum transmission can be determined first; then, based on the type of quantum hardware platform, the quantum noise monitoring model and the first error correction model can be adjusted.

[0140] As can be seen, this embodiment provides a hardware adaptation and optimization scheme. By adjusting the parameters of the quantum noise monitoring model and the reinforcement learning model for different types of quantum hardware platforms (such as superconducting quantum computers, ion trap systems, etc.), it is possible to better acquire quantum noise data and determine more suitable error correction strategies, thereby improving the hardware adaptability of the model.

[0141] In this application, the core objective of the reinforcement learning model is to train the model using real-time quantum noise data and optimize the error correction strategy in quantum communication based on the model output. The model's input is quantum noise data, and its output is the optimal error correction strategy.

[0142] This application provides a specific quantum error correction method. Figure 5 A schematic diagram of the structure of a quantum communication system provided in an embodiment of this application is shown. Figure 5 As shown, the quantum communication system includes a quantum noise monitoring model 501, an error correction model 502, an error correction operation execution module 503, and a communication quality index feedback module 504.

[0143] First, the noise state in the quantum transmission link is monitored in real time based on the quantum noise monitoring model 501. Data on noise characteristics such as qubit decoherence time, quantum gate operation error, and crosstalk between qubits are collected through sensors or quantum detectors. The monitoring data will be used for training the subsequent error correction model 502 and optimizing the error correction strategy.

[0144] After the quantum noise monitoring model 501 collects the data, it needs to be preprocessed. The noisy data needs to be denoised and standardized to ensure that it meets the input requirements of the reinforcement learning model.

[0145] Furthermore, the error correction model 502 is trained and optimized based on the quantum noise data collected by the quantum noise monitoring model 501. Through interaction with the quantum communication system, the model can adjust the error correction strategy according to real-time noise feedback. The input of the error correction model 502 is the noise state data, and the output is the optimal error correction strategy.

[0146] Based on the output of error correction model 502, an error correction strategy adapted to the current noise environment can be dynamically generated. The error correction strategy includes selecting different error correction codes (such as surface codes, distorted codes, etc.), adjusting quantum gate operations (such as operation order, period, etc.), or correcting errors by resetting qubits.

[0147] The error correction operation execution module 503 executes specific error correction operations based on the error correction strategy output by the error correction model 502. The communication quality index feedback module 504 obtains the communication quality index of the corresponding error correction operation. First, it feeds the obtained communication quality index back to the error correction operation execution module 503 to optimize the error correction operation. Through multiple iterations, it finds the optimal error correction operation under the current error correction strategy.

[0148] After the error correction operation is optimized, the communication quality index is fed back to the error correction model 502 to adjust the Q-value function of the error correction model 502, thereby feeding back to the reinforcement learning model. It can be seen that the quantum communication system of this embodiment will further adjust and optimize the error correction strategy based on the error feedback of quantum fidelity or transmission results to the reinforcement learning model, ensuring stability and reliability under long-term operation.

[0149] Reinforcement learning algorithm selection: This embodiment can employ reinforcement learning algorithms such as "deep Q-networks or proximal policy optimization". Deep Q-networks are suitable for handling high-dimensional state spaces and can learn stably through experience replay and target networks; proximal policy optimization has high stability and is particularly suitable for real-time policy adjustment and large-scale optimization problems.

[0150] The goal of reinforcement learning models is to maximize rewards. In each training epoch, the reinforcement learning model continuously adjusts its error correction strategy through interaction with the quantum communication system and optimizes it through feedback.

[0151] In this embodiment, the reward function is designed as a function of the data transmission error rate and quantum fidelity in quantum communication. A positive reward is given if the error correction strategy effectively reduces errors or improves quantum fidelity; conversely, a negative reward is given if the effect is poor. For example, if a certain error correction strategy reduces communication errors, the reward function can be defined as:

[0152] R t =-E t / E 总 (4)

[0153] Among them, R t E is the reward at time t; t E represents the data transmission error in quantum communication. 总 It is the largest error in quantum communication.

[0154] Assume the quantum fidelity of the system at time t is F. t The feedback adjustment formula for quantum fidelity is then:

[0155] F t+1 =F t +ΔF (5)

[0156] Here, ΔF is the feedback adjustment amount after optimization by the reinforcement learning model, reflecting the change in quantum fidelity.

[0157] As can be seen, this application significantly improves the reliability, stability, and data transmission quality of quantum communication by monitoring the noise state in the quantum transmission link in real time and dynamically adjusting the error correction strategy using reinforcement learning algorithms. Unlike traditional static error correction methods, this application can adaptively optimize the error correction strategy according to changes in the noise environment, effectively reducing the communication error rate, improving quantum fidelity, and optimizing resource utilization. Simulation results show that compared with traditional error correction methods, this application can reduce communication errors by about 20%, improve quantum fidelity by about 6%, reduce resource waste by about 10%, and improve system stability by about 6%. This method not only improves communication quality in quantum communication but also has broad application prospects, providing a more intelligent and efficient error correction solution in complex quantum communication networks.

[0158] Based on the quantum transmission error correction method provided in the above embodiments, correspondingly, this application also provides a quantum transmission error correction device. Figure 6 This is a schematic diagram of a quantum transmission error correction device provided in an embodiment of this application. Figure 6 As shown, the device may include the following modules:

[0159] The first acquisition module 601 is used to acquire quantum noise data in the quantum transmission link;

[0160] The input module 602 is used to input quantum noise data into the first error correction model to obtain the corresponding first error correction strategy; the first error correction model is trained based on a reinforcement learning model.

[0161] The correction module 603 is used to correct errors in the quantum transmission link based on the first error correction strategy, so as to obtain the quantum transmission link after the current correction.

[0162] The second acquisition module 604 is used to acquire the first communication quality index of the quantum transmission link after the current correction.

[0163] The determination module 605 is used to determine the reward value of the first error correction strategy based on the first communication quality index;

[0164] Adjustment module 606 is used to adjust the Q-value function in the first error correction model based on the reward value to obtain the second error correction model;

[0165] The update module 607 is used to update the first error correction model to the second error correction model and return the steps of acquiring quantum noise data in the quantum transmission link.

[0166] The quantum transmission error correction device of this application embodiment can adjust the Q-value function of the error correction model based on real-time feedback data. Therefore, the adjusted second error correction model can output a second error correction strategy that better meets the requirements based on the actual feedback. That is, this solution, through reinforcement learning algorithm, can adaptively adjust the model according to real-time correction feedback. This not only enables the error correction model to adapt to changes in the noise environment, but also provides an effective error correction strategy to solve the problems caused by noise, thereby improving the reliability, stability, and data processing quality of the quantum transmission link, as well as improving the performance of the execution device in complex noise environments.

[0167] In some feasible embodiments, the determining module 605 is specifically used for:

[0168] The reward value is determined based on the magnitude of the first communication quality indicator;

[0169] When the model adjustment objective is to improve the first communication quality index, the reward value is positively correlated with the first communication quality index; when the model adjustment objective is to reduce the first communication quality index, the reward value is negatively correlated with the first communication quality index.

[0170] In some feasible embodiments, the first communication quality metric includes quantum fidelity; correspondingly, the determining module 605 is specifically used for:

[0171] The reward value is determined based on the quantum fidelity; the reward value is positively correlated with the quantum fidelity.

[0172] In some feasible embodiments, the first communication quality indicator includes the data transmission error rate; correspondingly, the determining module 605 is specifically used for:

[0173] The reward value is determined based on the magnitude of the data transmission error rate; the reward value is negatively correlated with the data transmission error rate.

[0174] In some feasible embodiments, the correction module 603 is specifically configured to perform any one or any combination of the following steps:

[0175] Determine the target error correction code to be corrected; the target error correction code includes the surface code and the distortion code;

[0176] Adjust the order of quantum gate operations;

[0177] Adjust the period of quantum gate operations.

[0178] In some feasible embodiments, the correction module 603 is specifically used for:

[0179] Based on the first error correction strategy, determine the corresponding error correction operation;

[0180] Perform error correction;

[0181] Obtain the second communication quality index after performing the error correction operation;

[0182] If the second communication quality index does not meet the preset conditions, the error correction operation is updated based on the second communication quality index and the first error correction strategy, and the step of executing the error correction operation is returned until the second communication quality index meets the preset conditions, so as to obtain the current corrected quantum transmission link.

[0183] In some feasible embodiments, the adjustment module 606 is specifically used for:

[0184] Obtain the current Q-value, preset discount factor, and preset learning rate of the first error correction strategy;

[0185] Based on the preset discount factor, preset learning rate, reward value, and first Q value, determine the second Q value corresponding to the first error correction strategy;

[0186] The second error correction model is determined based on the second Q value.

[0187] In some feasible embodiments, the first acquisition module 601 is specifically used for:

[0188] Obtain raw quantum noise data;

[0189] Extract the feature data from the original quantum noise data to obtain quantum noise data; the feature data includes any one or any combination of the following: decoherence time data, quantum gate operation error data, and bit crosstalk data.

[0190] In some feasible embodiments, the second acquisition module 604 is specifically used for:

[0191] Quantum noise data is obtained based on a quantum noise monitoring model;

[0192] Before acquiring quantum noise data in the quantum transmission link, the method also includes:

[0193] Determine the quantum hardware platform for performing quantum teleportation;

[0194] Based on the type of quantum hardware platform, the quantum noise monitoring model and the first error correction model are adjusted.

[0195] The quantum transmission error correction device provided in this application corresponds to the quantum transmission error correction method provided in the above embodiments. The two have the same embodiments and beneficial effects, which will not be repeated here.

[0196] Figure 7 A schematic diagram of a quantum transmission error correction device provided in an embodiment of this application is shown. Figure 7 As shown, the quantum transmission error correction device may include a processor 701 and a memory 702 storing computer program instructions.

[0197] Specifically, the processor 701 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0198] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.

[0199] Memory 702 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory 702 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0200] The processor 701 implements any of the quantum transmission error correction methods described in the above embodiments by reading and executing computer program instructions stored in the memory 702.

[0201] In one example, the quantum transmission error correction device may also include a communication interface 703 and a bus 704. Wherein, as Figure 3 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 704 and complete communication with each other.

[0202] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0203] Bus 704 includes hardware, software, or both, that couples the components of a quantum transmission error correction device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA Local Bus, VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 704 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0204] The quantum transmission error correction device can execute the quantum transmission error correction method in the embodiments of this application, thereby realizing the quantum transmission error correction method combined with the above description.

[0205] Furthermore, in conjunction with the quantum transmission error correction methods described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the quantum transmission error correction methods described in the above embodiments.

[0206] This application also provides a computer program product, including a computer program, which, when executed, implements any of the quantum transmission error correction methods described in the above embodiments.

[0207] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0208] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0209] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0210] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0211] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A quantum transmission error correction method, characterized in that, include: Acquire quantum noise data in a quantum transmission link; The quantum noise data is input into the first error correction model to obtain the corresponding first error correction strategy; The first error correction model is trained based on a reinforcement learning model; Based on the first error correction strategy, the errors in the quantum transmission link are corrected to obtain the current corrected quantum transmission link. Obtain the first communication quality index of the quantum transmission link after the current correction; Based on the first communication quality index, determine the reward value of the first error correction strategy; Based on the reward value, the Q-value function in the first error correction model is adjusted to obtain the second error correction model; Update the first error correction model to the second error correction model, and return to the step of obtaining quantum noise data in the quantum transmission link.

2. The quantum transmission error correction method according to claim 1, characterized in that, Based on the first communication quality indicator, the reward value of the first error correction strategy is determined, including: The reward value is determined based on the magnitude of the first communication quality index. When the model adjustment objective is to improve the first communication quality index, the reward value is positively correlated with the first communication quality index. When the model adjustment objective is to reduce the first communication quality index, the reward value is negatively correlated with the first communication quality index.

3. The quantum transmission error correction method according to claim 2, characterized in that, The first communication quality indicator includes quantum fidelity; Correspondingly, based on the first communication quality indicator, the reward value of the first error correction strategy is determined, including: The reward value is determined based on the magnitude of the quantum fidelity. The reward value is positively correlated with the quantum fidelity.

4. The quantum transmission error correction method according to claim 2, characterized in that, The first communication quality indicator includes the data transmission error rate; Correspondingly, based on the first communication quality indicator, the reward value of the first error correction strategy is determined, including: The reward value is determined based on the magnitude of the data transmission error rate. The reward value is negatively correlated with the data transmission error rate.

5. The quantum transmission error correction method according to any one of claims 1 to 4, characterized in that, Based on the first error correction strategy, errors in the quantum transmission link are corrected, including any one or any combination of the following: Determine the target error correction code to be corrected; The target error correction code includes a surface code and a distortion code; Adjust the order of quantum gate operations; Adjust the period of quantum gate operations.

6. The quantum transmission error correction method according to claim 5, characterized in that, Based on the first error correction strategy, errors in the quantum transmission link are corrected to obtain the current corrected quantum transmission link, including: Based on the first error correction strategy, determine the corresponding error correction operation; Perform the error correction operation; Obtain the second communication quality index after performing the error correction operation; If the second communication quality index does not meet the preset conditions, the error correction operation is updated based on the second communication quality index and the first error correction strategy, and the step of executing the error correction operation is returned until the second communication quality index meets the preset conditions, so as to obtain the current corrected quantum transmission link.

7. The quantum transmission error correction method according to any one of claims 1 to 4, characterized in that, Based on the reward value, the Q-value function in the first error correction model is adjusted to obtain the second error correction model, including: Obtain the current Q-value, preset discount factor, and preset learning rate of the first error correction strategy; Based on the preset discount factor, the preset learning rate, the reward value, and the first Q value, determine the second Q value corresponding to the first error correction strategy; The second error correction model is determined based on the second Q value.

8. The quantum transmission error correction method according to any one of claims 1 to 4, characterized in that, The acquisition of quantum noise data in the quantum transmission link includes: Obtain raw quantum noise data; The quantum noise data is obtained by extracting the feature data from the original quantum noise data; the feature data includes any one or any combination of the following: decoherence time data, quantum gate operation error data, and bit crosstalk data.

9. The quantum transmission error correction method according to any one of claims 1 to 4, characterized in that, The acquisition of quantum noise data in the quantum transmission link includes: The quantum noise data is obtained based on a quantum noise monitoring model; Prior to acquiring quantum noise data in the quantum transmission link, the method further includes: Determine the quantum hardware platform to perform the quantum teleportation; The quantum noise monitoring model and the first error correction model are adjusted based on the type of the quantum hardware platform.

10. A quantum transmission error correction device, characterized in that, The device includes: The first acquisition module is used to acquire quantum noise data in the quantum transmission link; The input module is used to input the quantum noise data into the first error correction model to obtain the corresponding first error correction strategy; the first error correction model is trained based on a reinforcement learning model. The correction module is used to correct the errors in the quantum transmission link based on the first error correction strategy, so as to obtain the quantum transmission link after the current correction. The second acquisition module is used to acquire the first communication quality index of the quantum transmission link after the current correction. The determination module is used to determine the reward value of the first error correction strategy based on the first communication quality index; An adjustment module is used to adjust the Q-value function in the first error correction model based on the reward value to obtain a second error correction model; An update module is used to update the first error correction model to a second error correction model and return to the step of obtaining quantum noise data in the quantum transmission link.

11. A quantum transmission error correction device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the quantum transmission error correction method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the quantum transmission error correction method as described in any one of claims 1 to 9.

13. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the quantum transmission error correction method as described in any one of claims 1 to 9.

Images