A method, system, medium, and electronic device for continuous-variable quantum key distribution parameter optimization based on GA-BP neural network.

Abstract

This invention provides a method, system, medium, and electronic device for optimizing continuous variable quantum key distribution parameters based on a GA-BP neural network. The method includes: acquiring raw data from a CV-QKD system; normalizing the raw data to obtain normalized data; dividing the normalized data into a training set and a test set; establishing a BP neural network using the training set; optimizing the weights and thresholds of the BP neural network using a genetic algorithm to obtain a GA-BP neural network with optimal fitting performance; testing the GA-BP neural network using the test set to obtain a tested GA-BP neural network; obtaining the mapping relationship between the target modulation variance and the corresponding target key rate based on the tested GA-BP neural network; and using a bisection method based on the mapping relationship to find the optimal modulation variance and the corresponding maximum key rate. This invention reduces the complexity of key rate calculation and improves the key rate and transmission distance in the CV-QKD system.

Description

Technical Field

[0001] This invention relates to the field of quantum communication, to artificial intelligence, and particularly to neural networks. Specifically, it relates to a method, system, medium, and electronic device for optimizing continuous variable quantum key distribution parameters based on a GA-BP neural network. Background Technology

[0002] Quantum key distribution (QKD), as a one-time key distribution method, has become one of the best solutions for ensuring absolute security. QKD enables two remotely separated parties, Alice and Bob, to extract a symmetric key string using a quantum channel. Currently, based on the implementation method, quantum key distribution can be divided into discrete variable quantum key distribution (DVQKD) and continuous variable quantum key distribution (CVQKD). CV-QKD systems have advantages such as high key rates and convenient signal carrier preparation. Therefore, CV-QKD technology is relatively mature, has low cost, and can be transmitted in ordinary optical fibers along with classical optical communication, offering significant practical advantages.

[0003] Key rate and transmission distance are crucial parameters for evaluating the performance of CV-QKD systems. For decades, key rate calculations have typically relied on numerical analysis methods. For instance, when analyzing quantum light transmission through atmospheric channels, researchers have largely employed numerical methods to analyze the impact of transmittance and noise on the key rate. While numerical methods are widely used in practice to calculate key rates for many quantum key distribution protocols, they are computationally intensive and time-consuming. Therefore, developing tools for calculating key rates more efficiently than numerical methods is of paramount importance. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, medium, and electronic device for optimizing continuous variable quantum key distribution parameters based on GA-BP neural network, in order to solve the problems mentioned in the background art.

[0005] In a first aspect, the present invention provides a continuous variable quantum key distribution parameter optimization method based on a GA-BP neural network. The method includes: acquiring raw data from a CV-QKD system and normalizing the raw data to obtain normalized data; dividing the normalized data into a training set and a test set; wherein the raw data includes at least two modulation variances and at least two key rates; one modulation variance corresponds to one key rate; the normalized data includes at least two normalized modulation variances and at least two normalized key rates; both the training set and the test set include at least one normalized modulation variance and a corresponding at least one normalized key rate; using the normalized modulation variance in the training set as an input variable, the training... A BP neural network is established using the normalized key rate of the test set as the output variable. A genetic algorithm is used to optimize the weights and thresholds of the BP neural network to obtain the GA-BP neural network with the best fitting effect. The normalized modulation variance of the test set is used as the input variable of the GA-BP neural network, and the normalized key rate of the test set is used as the output variable of the GA-BP neural network to test the GA-BP neural network and obtain a well-tested GA-BP neural network. Based on the well-tested GA-BP neural network, the mapping relationship between the target modulation variance and the corresponding target key rate is obtained. Based on this mapping relationship, a binary search method is used to find the optimal modulation variance and the corresponding maximum key rate, thus achieving continuous variable quantum key distribution parameter optimization.

[0006] This invention proposes a continuous variable quantum key distribution parameter optimization method based on GA-BP neural network to analyze the key rate of CV-QKD system under finite code length conditions. It applies a backpropagation neural network (GA-BP) optimized by a genetic algorithm to the CV-QKD system, and then uses a binary search method to find the optimal modulation variance corresponding to the maximum key rate. By utilizing machine learning techniques to optimize the parameters of the CV-QKD system, the complexity of calculating the key rate in existing technologies is reduced, and the key rate and transmission distance in the CV-QKD system are improved to a certain extent.

[0007] In one implementation of the first aspect, the modulation variance is a positive number.

[0008] In one implementation of the first aspect, the BP neural network is a three-layer BP neural network, comprising an input layer, a hidden layer, and an output layer, wherein the formula for calculating the number of neurons in the hidden layer is:

[0009]

[0010] Where n1 represents the number of neurons in the hidden layer; n represents the number of neurons in the input layer; m represents the number of neurons in the output layer; and a is a constant from 1 to 10.

[0011] In one implementation of the first aspect, the step of using a genetic algorithm to optimize the weights and thresholds of the BP neural network to obtain a GA-BP neural network with optimal fitting performance includes: generating an initial population; the initial population includes multiple individuals; each individual represents a parameter set of the BP neural network; each parameter set includes a weight and a threshold; determining the termination condition of the genetic algorithm, initializing the genetic algorithm and calculating the fitness value of each individual; determining whether the genetic algorithm has reached the termination condition based on the fitness value; and when it is determined that the genetic algorithm has not reached the termination condition, resetting the genetic algorithm. The genetic algorithm sequentially performs selection, crossover, and mutation operations to generate a new generation of chromosomes until the genetic algorithm reaches the termination condition. When the genetic algorithm reaches the termination condition, the calculation of the fitness value ends, and the weights and thresholds optimized by the genetic algorithm are obtained. The BP neural network is then used to adjust the weights and thresholds optimized by the genetic algorithm to improve their accuracy until the accuracy meets a preset accuracy requirement. Finally, the adjusted weights and thresholds are obtained, and the GA-BP neural network is obtained based on the adjusted weights and thresholds.

[0012] In one implementation of the first aspect, the GA-BP neural network uses the hyperbolic tangent function as the activation function.

[0013] In this implementation, the hyperbolic tangent function tanh is used as the activation function. Since tanh is a concave function in the positive domain, the key rate obtained by passing the modulation variance through the GA-BP neural network is also a concave function of the modulation variance.

[0014] In one implementation of the first aspect, the mapping relationship is as follows: the target key rate is a concave function of the target modulation variance; finding the optimal modulation variance and the corresponding maximum key rate based on the mapping relationship using a bisection method includes: representing the mapping relationship using a relational graph; the relational graph is a schematic diagram of the concave function; wherein the target key rate is the vertical axis and the target modulation variance is the horizontal axis; on the relational graph, starting from a preset initial range of the target modulation variance, calculating the midpoint of the preset initial range, and calculating the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint; adjusting the range of the target modulation variance according to the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint, and repeating this process until the target modulation variance corresponding to the maximum target key rate is found; the maximum target key rate is the maximum key rate; the target modulation variance corresponding to the maximum target key rate is the optimal modulation variance.

[0015] Secondly, the present invention provides a continuous variable quantum key distribution parameter optimization system based on a GA-BP neural network. The system includes: a normalization module, used to acquire raw data from a CV-QKD system and normalize the raw data to obtain normalized data, dividing the normalized data into a training set and a test set; wherein the raw data includes at least two modulation variances and at least two key rates; one modulation variance corresponds to one key rate; the normalized data includes at least two normalized modulation variances and at least two normalized key rates; both the training set and the test set include at least one normalized modulation variance and a corresponding at least one normalized key rate; and a network establishment module, used to take the normalized modulation variance in the training set as an input variable, and the normalized data in the training set... The system employs a GA-BP neural network, using the normalized key rate as the output variable. A network optimization module optimizes the weights and thresholds of the BP neural network using a genetic algorithm to obtain the optimal GA-BP neural network. A network testing module uses the normalized modulation variance in the test set as the input variable and the normalized key rate in the test set as the output variable to test the GA-BP neural network and obtain a well-tested GA-BP neural network. A parameter optimization module obtains the mapping relationship between the target modulation variance and the corresponding target key rate based on the tested GA-BP neural network, and uses a binary search method based on this mapping relationship to find the optimal modulation variance and the corresponding maximum key rate, thus optimizing the continuous variable quantum key distribution parameters.

[0016] Thirdly, the present invention provides an electronic device comprising: a processor and a memory; the memory for storing a computer program; and the processor for executing the computer program stored in the memory to enable the electronic device to execute the above-described continuous variable quantum key distribution parameter optimization method based on a GA-BP neural network.

[0017] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-described method for optimizing continuous variable quantum key distribution parameters based on a GA-BP neural network.

[0018] As described above, the continuous variable quantum key distribution parameter optimization method, system, medium, and electronic device based on GA-BP neural network of the present invention have the following beneficial effects:

[0019] (1) Compared with the prior art, the present invention optimizes the parameters of the CV-QKD system by using a GA-BP-based method, wherein the key rate can be effectively obtained from the trained GA-BP neural network given the modulation variance; secondly, since GA-BP uses the hyperbolic tangent function as the activation function and the key rate is derived from the GA-BP neural network, it is concave in the modulation variance. This theoretical result enables the use of the bisection method to effectively find the maximum key rate and the optimal modulation variance of a given GA-BP neural network.

[0020] (2) The continuous variable quantum key distribution parameter optimization scheme based on GA-BP neural network proposed in this invention can improve the key rate to a certain extent. The research on this scheme has profound significance for the practical application of CV-QKD system and further promotes the development of CV-QKD system in practical application. Attached Figure Description

[0021] Figure 1 The flowchart shown is a continuous variable quantum key distribution parameter optimization method based on GA-BP neural network according to an embodiment of the present invention.

[0022] Figure 2 The flowchart shown is a process of using a genetic algorithm to optimize the weights and thresholds of the BP neural network to obtain the GA-BP neural network with the best fitting effect, as described in an embodiment of the present invention.

[0023] Figure 3 The diagram shown is a schematic representation of the continuous variable quantum key distribution parameter optimization system based on the GA-BP neural network described in an embodiment of the present invention. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] For CV-QKD systems, the key rate for finite code lengths is calculated as follows:

[0027]

[0028] Where N represents the total number of signals exchanged by Alice and Bob during the protocol process; n is the number of segments of the N signals used to establish the key; Δ(n) is related to the security of privacy amplification; FER represents the frame error rate; negotiation efficiency is measured by β = R / C(s), where R is the code rate and C(s) is the channel capacity at the signal-to-noise ratio (SNR) value s; I AB The mutual information between Alice and Bob is represented by χ; the maximum value of the Holevo information leaked to Eve is represented by χ. BE Indicated. Mutual information I AB Holevo world χ BE All of these are related to modulation variance. Therefore, by focusing on effectively optimizing modulation variance and maximizing the key rate, it is ultimately possible to improve the performance of CV-QKD.

[0029] With the simultaneous rise of artificial intelligence and quantum information science, these two fields are converging in a synergistic manner. Within this growing trend, some works attempt to design new theoretical models based on quantum algorithms to improve classical machine learning and achieve the desired quantum speedup. Simultaneously, as the complexity of quantum systems continues to increase, advanced quantum information technologies also require powerful data processing and analysis tools. Therefore, there is an urgent need to leverage existing classical machine learning techniques to solve practical but challenging problems in quantum information science, such as tomography, quantum state classification, quantum metrology, quantum control, and quantum cryptography.

[0030] See Figures 1 to 3The following embodiments of this invention provide a method, system, medium, and electronic device for optimizing continuous variable quantum key distribution parameters based on a GA-BP neural network. The method optimizes the parameters of a CV-QKD system using a GA-BP-based approach. Given a modulation variance, the key rate can be effectively obtained from a trained GA-BP neural network. Furthermore, since GA-BP uses a hyperbolic tangent function as the activation function, and the key rate is derived from the GA-BP neural network, the modulation variance is concave. This theoretical result allows for the efficient use of a bisection method to find the maximum key rate and optimal modulation variance for a given GA-BP neural network. The proposed continuous variable quantum key distribution parameter optimization scheme based on a GA-BP neural network can improve the key rate to a certain extent. The research on this scheme has profound significance for the practical application of CV-QKD systems and further promotes the development of CV-QKD systems in practical applications.

[0031] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0032] like Figure 1 As shown, in one embodiment, the present invention provides a continuous variable quantum key distribution parameter optimization method based on a GA-BP neural network, the method comprising:

[0033] Step S1: Obtain raw data from the CV-QKD system, normalize the raw data to obtain normalized data, and divide the normalized data into training set and test set.

[0034] The original data includes at least two modulation variances and at least two key rates; each modulation variance corresponds to a key rate; the normalized data includes at least two normalized modulation variances and at least two normalized key rates; both the training set and the test set include at least one normalized modulation variance and at least one corresponding normalized key rate.

[0035] In one embodiment, the modulation variance is a positive number.

[0036] Step S2: Using the normalized modulation variance in the training set as the input variable and the normalized key rate in the training set as the output variable, a BP neural network is established.

[0037] In one embodiment, the BP neural network is a three-layer BP neural network, comprising an input layer, a hidden layer, and an output layer, wherein the formula for calculating the number of neurons in the hidden layer is:

[0038]

[0039] Where n1 represents the number of neurons in the hidden layer; n represents the number of neurons in the input layer; m represents the number of neurons in the output layer; and a is a constant from 1 to 10.

[0040] It should be noted that the specific value of 'a' is not a limiting factor for this invention, as long as it is between 1 and 10. In practical applications, it is limited by the complexity of the task and the risk of overfitting.

[0041] In this embodiment, a method for calculating neuron data of hidden layers based on the input layer and the input layer size is provided.

[0042] It should be noted that the BP (Back Propagation) neural network is a concept proposed in 1986 by scientists led by Rumelhart and McClelland. It is a multi-layer feedforward neural network trained according to the error backpropagation algorithm.

[0043] Step S3: Use a genetic algorithm to optimize the weights and thresholds of the BP neural network to obtain the GA-BP neural network with the best fitting effect.

[0044] It should be noted that the Genetic Algorithm (GA) was first proposed by John Holland in the United States in the 1970s. This algorithm is designed based on the laws of biological evolution in nature. It is a computational model that simulates the biological evolutionary process of natural selection and genetic mechanisms in Darwin's theory of evolution. It is a method for searching for optimal solutions by simulating the natural evolutionary process. This algorithm uses mathematical methods and computer simulation to transform the problem-solving process into processes similar to the crossover and mutation of chromosomes and genes in biological evolution. When solving complex combinatorial optimization problems, it can usually obtain better optimization results faster than some conventional optimization algorithms. Genetic algorithms have been widely used in combinatorial optimization, machine learning, signal processing, adaptive control, and artificial life.

[0045] like Figure 2 As shown, in one embodiment, the step of using a genetic algorithm to optimize the weights and thresholds of the BP neural network to obtain the GA-BP neural network with the best fitting effect includes:

[0046] Step S301: Generate the initial population.

[0047] In this embodiment, the initial population includes multiple individuals; each individual represents a parameter set of the BP neural network; each parameter set includes a weight and a threshold.

[0048] Step S302: Determine the termination condition of the genetic algorithm, initialize the genetic algorithm, and calculate the fitness value of each individual.

[0049] It should be noted that the fitness value represents how well an individual performs in the problem. The calculation of the fitness value directly affects the selection operation of the genetic algorithm, thereby determining which individuals will be retained and generate the next generation. In this embodiment, the calculation of the fitness value adopts conventional technical means in the field, so its working principle will not be described in detail here.

[0050] Step S303: Determine whether the genetic algorithm has reached the termination condition based on the fitness value.

[0051] If it is determined that the genetic algorithm has not reached the termination condition, step S304 is executed.

[0052] Step S304: Perform selection, crossover, and mutation operations sequentially on the genetic algorithm to generate a new generation of chromosomes until the genetic algorithm reaches the termination condition.

[0053] When it is determined that the genetic algorithm has reached the termination condition, step S305 is executed.

[0054] Step S305: End the calculation of the fitness value, obtain the weights and thresholds optimized by the genetic algorithm, and use the BP neural network to adjust the weights and thresholds optimized by the genetic algorithm to improve the accuracy of the weights and thresholds optimized by the genetic algorithm until the accuracy meets the preset accuracy requirement, obtain the adjusted weights and thresholds, and obtain the GA-BP neural network based on the adjusted weights and thresholds.

[0055] It should be noted that the specific setting of this preset precision requirement is not a limitation of the present invention. In practical applications, it can be set according to the specific application scenario.

[0056] Step S4: Use the normalized modulation variance in the test set as the input variable of the GA-BP neural network, and the normalized key rate in the test set as the output variable of the GA-BP neural network, so as to test the GA-BP neural network and obtain a tested GA-BP neural network.

[0057] It should be noted that the testing of the GA-BP neural network in step S4 uses conventional techniques in the field of neural networks, so its working principle will not be described in detail here. The test can evaluate the performance of the GA-BP neural network and verify its generalization ability on unseen data.

[0058] Specifically, by using the normalized modulation variance in the training set as the input variable and the normalized key rate in the training set as the output variable, a BP neural network is established. Then, the weights and thresholds of the BP neural network are optimized using a genetic algorithm to obtain a GA-BP neural network. Finally, by using the normalized modulation variance in the test set as the input variable and the normalized key rate in the test set as the output variable, the GA-BP neural network is tested to train it to learn the mapping relationship between the target modulation variance and the target key rate.

[0059] In one embodiment, the GA-BP neural network uses the hyperbolic tangent function as the activation function tanh.

[0060] It should be noted that since tanh is a concave function in the positive number domain, the key rate obtained by passing the modulation variance through the GA-BP neural network is also a concave function of the modulation variance.

[0061] In one embodiment, the mapping relationship is: the target key rate is a concave function of the target modulation variance.

[0062] This invention optimizes the parameters of a CV-QKD system using a GA-BP-based method, where the key rate can be efficiently derived from a trained GA-BP neural network given the modulation variance. Furthermore, since GA-BP uses a hyperbolic tangent function as the activation function, and the key rate is derived from the GA-BP neural network, it is concave in terms of modulation variance. This theoretical result allows us to efficiently find the maximum key rate and optimal modulation variance for a given GA-BP neural network using a bisection method.

[0063] Step S5: Obtain the mapping relationship between the target modulation variance and the corresponding target key rate based on the tested GA-BP neural network, and use the binary search method based on the mapping relationship to find the optimal modulation variance and the corresponding maximum key rate, thereby optimizing the continuous variable quantum key distribution parameters.

[0064] In one embodiment, finding the optimal modulation variance and the corresponding maximum key rate using a bisection method based on the mapping relationship includes: representing the mapping relationship using a graph; the graph being a schematic diagram of the concave function; wherein the target key rate is the vertical axis and the target modulation variance is the horizontal axis; on the graph, starting from a preset initial range of the target modulation variance, calculating the midpoint of the preset initial range, and calculating the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint; adjusting the range of the target modulation variance according to the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint, and repeating this process until the target modulation variance corresponding to the maximum target key rate is found; the maximum target key rate is the maximum key rate; the target modulation variance corresponding to the maximum target key rate is the optimal modulation variance.

[0065] It should be noted that the preset initial range is a fixed range that is set in advance. The specific value of this range is not a limitation of the present invention. In practical applications, it can be set according to the specific application scenario. For example, in one embodiment, the preset initial range is set to 0 to 20.

[0066] In this embodiment, the calculation of the target key rate corresponding to the midpoint and the target key rate of the adjacent points on both sides of the midpoint can be referred to the above key rate calculation formula, so it will not be described in detail here.

[0067] It should be noted that this invention proposes a continuous variable quantum key distribution parameter optimization method based on GA-BP neural network to analyze the key rate of CV-QKD system under finite code length conditions. During parameter optimization, an error correction code and code rate are given, and the lower bound of the frame error rate is used as the FER value in the key rate. A backpropagation neural network (GA-BP) optimized based on a genetic algorithm is applied to the CV-QKD system, and then a binary search method is used to find the optimal modulation variance corresponding to the maximum key rate. By utilizing machine learning techniques to optimize the parameters of the CV-QKD system, the complexity of calculating the key rate in existing technologies is reduced, and the key rate and transmission distance in the CV-QKD system are improved to a certain extent.

[0068] The scope of protection of the continuous variable quantum key distribution parameter optimization method based on GA-BP neural network described in this embodiment is not limited to the execution order of the steps listed in this embodiment. Any scheme implemented by adding, subtracting, or replacing steps in the prior art based on the principle of this invention is included within the scope of protection of this invention.

[0069] This invention also provides an electronic device, which includes a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device executes the above-described continuous variable quantum key distribution parameter optimization method based on GA-BP neural network.

[0070] This invention also provides a computer-readable storage medium storing a computer program that, when executed by an electronic device, implements the above-described method for optimizing continuous variable quantum key distribution parameters based on a GA-BP neural network.

[0071] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0072] This invention also provides a continuous variable quantum key distribution parameter optimization system based on a GA-BP neural network. This system can implement the continuous variable quantum key distribution parameter optimization method based on a GA-BP neural network described in this invention. However, the implementation apparatus for the continuous variable quantum key distribution parameter optimization method based on a GA-BP neural network described in this invention includes, but is not limited to, the structure of the continuous variable quantum key distribution parameter optimization system based on a GA-BP neural network listed in this embodiment. Any structural modifications and substitutions of the prior art made according to the principles of this invention are included within the protection scope of this invention.

[0073] like Figure 3 As shown, in one embodiment, the present invention provides a continuous variable quantum key distribution parameter optimization system based on a GA-BP neural network, the system comprising:

[0074] The normalization module 31 is used to obtain raw data from the CV-QKD system, normalize the raw data to obtain normalized data, and divide the normalized data into a training set and a test set; wherein, the raw data includes at least two modulation variances and at least two key rates; one modulation variance corresponds to one key rate; the normalized data includes at least two normalized modulation variances and at least two normalized key rates; both the training set and the test set include at least one normalized modulation variance and at least one corresponding normalized key rate.

[0075] The network establishment module 32 is used to establish a BP neural network by taking the normalized modulation variance in the training set as the input variable and the normalized key rate in the training set as the output variable.

[0076] The network optimization module 33 is used to optimize the weights and thresholds of the BP neural network using a genetic algorithm to obtain the GA-BP neural network with the best fitting effect.

[0077] The network testing module 34 is used to use the normalized modulation variance in the test set as the input variable of the GA-BP neural network and the normalized key rate in the test set as the output variable of the GA-BP neural network, so as to test the GA-BP neural network and obtain a tested GA-BP neural network.

[0078] The parameter optimization module 35 is used to obtain the mapping relationship between the target modulation variance and the corresponding target key rate based on the tested GA-BP neural network, and to find the optimal modulation variance and the corresponding maximum key rate using the binary search method based on the mapping relationship, thereby realizing the optimization of continuous variable quantum key distribution parameters.

[0079] It should be noted that the structure and principle of the normalization module 31, the network establishment module 32, the network optimization module 33, the network testing module 34, and the parameter optimization module 35 correspond one-to-one with the steps (steps S1 to S5) in the above-mentioned continuous variable quantum key distribution parameter optimization method based on GA-BP neural network. The specific working principle can also be referred to the introduction of the continuous variable quantum key distribution parameter optimization method based on GA-BP neural network in the foregoing embodiments, so it will not be repeated here.

[0080] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0081] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0082] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0084] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A continuous-variable quantum key distribution parameter optimization method based on GA-BP neural network, characterized in that, The method includes: Raw data is obtained from the CV-QKD system and normalized to obtain normalized data. The normalized data is then divided into a training set and a test set. The raw data includes at least two modulation variances and at least two key rates, with each modulation variance corresponding to a key rate. The normalized data includes at least two normalized modulation variances and at least two normalized key rates. Both the training set and the test set include at least one normalized modulation variance and a corresponding at least one normalized key rate. A BP neural network is established by using the normalized modulation variance in the training set as the input variable and the normalized key rate in the training set as the output variable. The weights and thresholds of the BP neural network are optimized using a genetic algorithm to obtain the GA-BP neural network with the best fitting effect; The normalized modulation variance in the test set is used as the input variable of the GA-BP neural network, and the normalized key rate in the test set is used as the output variable of the GA-BP neural network, so as to test the GA-BP neural network and obtain a well-tested GA-BP neural network. The mapping relationship between the target modulation variance and the corresponding target key rate is obtained based on the tested GA-BP neural network. The optimal modulation variance and the corresponding maximum key rate are found using the binary search method based on the mapping relationship, thereby optimizing the continuous variable quantum key distribution parameters.

2. The method for optimizing continuous-variable quantum key distribution parameters based on a GA-BP neural network according to claim 1, characterized in that, The modulation variance is a positive number.

3. The continuous variable quantum key distribution parameter optimization method based on GA-BP neural network according to claim 1, characterized in that, The BP neural network is a three-layer BP neural network, comprising an input layer, a hidden layer, and an output layer. The formula for calculating the number of neurons in the hidden layer is: Where n1 represents the number of neurons in the hidden layer; n represents the number of neurons in the input layer; m represents the number of neurons in the output layer; and a is a constant from 1 to 10.

4. The continuous variable quantum key distribution parameter optimization method based on GA-BP neural network according to claim 1, characterized in that, The step of using a genetic algorithm to optimize the weights and thresholds of the BP neural network to obtain the GA-BP neural network with the best fitting effect includes: An initial population is generated; the initial population includes multiple individuals; each individual represents a parameter set of the BP neural network; each parameter set includes a weight and a threshold. Determine the termination condition of the genetic algorithm, initialize the genetic algorithm, and calculate the fitness value of each individual; Based on the fitness value, determine whether the genetic algorithm has reached the termination condition; When it is determined that the genetic algorithm has not reached the termination condition, selection, crossover, and mutation operations are sequentially performed on the genetic algorithm to generate a new generation of chromosomes until it is determined that the genetic algorithm has reached the termination condition. When the genetic algorithm reaches the termination condition, the calculation of the fitness value ends, the weights and thresholds optimized by the genetic algorithm are obtained, and the BP neural network is used to adjust the weights and thresholds optimized by the genetic algorithm to improve the accuracy of the weights and thresholds optimized by the genetic algorithm until the accuracy meets the preset accuracy requirement. Then, the adjusted weights and thresholds are obtained, and the GA-BP neural network is obtained based on the adjusted weights and thresholds.

5. The method for optimizing continuous-variable quantum key distribution parameters based on a GA-BP neural network according to claim 1, characterized in that, The GA-BP neural network uses the hyperbolic tangent function as the activation function.

6. The method for optimizing continuous-variable quantum key distribution parameters based on a GA-BP neural network according to claim 1, characterized in that, The mapping relationship is as follows: the target key rate is a concave function of the target modulation variance; finding the optimal modulation variance and the corresponding maximum key rate using a binary search method based on the mapping relationship includes: The mapping relationship is represented by a graph; the graph is a schematic diagram of the concave function; wherein the target key rate is the vertical axis and the target modulation variance is the horizontal axis. On the relationship diagram, starting from a preset initial range of the target modulation variance, the midpoint of the preset initial range is calculated, and the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint are calculated. The range of the target modulation variance is adjusted based on the target key rate corresponding to the midpoint and the target key rates of the adjacent points on both sides of the midpoint, and this process is repeated until the target modulation variance corresponding to the maximum target key rate is found; the maximum target key rate is the maximum key rate; the target modulation variance corresponding to the maximum target key rate is the optimal modulation variance.

7. A continuous-variable quantum key distribution parameter optimization system based on a GA-BP neural network, characterized in that, The system includes: A normalization module is used to acquire raw data from the CV-QKD system, normalize the raw data to obtain normalized data, and divide the normalized data into a training set and a test set. The raw data includes at least two modulation variances and at least two key rates; each modulation variance corresponds to a key rate. The normalized data includes at least two normalized modulation variances and at least two normalized key rates. Both the training set and the test set include at least one normalized modulation variance and a corresponding at least one normalized key rate. The network establishment module is used to establish a BP neural network by taking the normalized modulation variance in the training set as the input variable and the normalized key rate in the training set as the output variable. The network optimization module is used to optimize the weights and thresholds of the BP neural network using a genetic algorithm to obtain the GA-BP neural network with the best fitting effect. The network testing module is used to use the normalized modulation variance in the test set as the input variable of the GA-BP neural network and the normalized key rate in the test set as the output variable of the GA-BP neural network, so as to test the GA-BP neural network and obtain a well-tested GA-BP neural network. The parameter optimization module is used to obtain the mapping relationship between the target modulation variance and the corresponding target key rate based on the tested GA-BP neural network, and to find the optimal modulation variance and the corresponding maximum key rate using the binary search method based on the mapping relationship, thereby realizing the optimization of continuous variable quantum key distribution parameters.

8. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the electronic device to perform the continuous variable quantum key distribution parameter optimization method based on the GA-BP neural network as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by an electronic device, the program implements the continuous variable quantum key distribution parameter optimization method based on the GA-BP neural network as described in any one of claims 1 to 6.

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