Parameter optimization method, device, equipment, medium and product

Abstract

The present disclosure provides a parameter optimization method, device, equipment, medium and product, wherein the quantum key distribution parameter optimization method includes: creating particles based on preset parameters of the PSO algorithm; initializing the individual optimal position and the global optimal position of any particle in the particle group; dividing the particle group into multiple particle clusters based on preset parameters, and the particle clusters include global particle clusters and local particle clusters; performing iterative calculations on the positions of the particles under the PSO algorithm based on preset parameters, initialized position information and particle clusters, and updating preset parameters, individual optimal positions and global optimal positions during the iterative calculation process; when it is detected that the iteration end condition is met, outputting the individual optimal position result, the global optimal position result and the key rate result corresponding to the global optimal position result according to the iterative calculation result. Through the embodiments of the present disclosure, the global optimal solution can be quickly found without falling into the local optimum, thereby improving the efficiency and reliability of the optimization parameters.

Description

Background Art

[0002] At present, under the condition of limited code length, the globally optimized parameters can significantly improve the security code rate of the actual decoy-state QKD system.

[0003] In the related art, the traditional local search algorithm (LSA) can be used to find the optimal parameters.

[0004] However, with the massive increase in optimization parameters, the time complexity of the local search algorithm grows exponentially with the number of optimization parameters, and it will fall into the local optimum and consume more time and computing resources. It cannot meet the real-time parameter optimization of high-speed QKD systems, nor can it meet the needs of resource optimization configuration in large-scale quantum communication networks.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the present disclosure is to provide a parameter optimization method, device, equipment, medium and product, which are used to overcome the problems of low parameter optimization efficiency caused by the limitations and defects of related technologies, at least to a certain extent.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for optimizing quantum key distribution parameters is provided, comprising: creating particles based on preset parameters of a PSO algorithm; initializing the individual best position and the global best position of any particle of the particle group to obtain initialization position information; dividing the particle group into multiple particle clusters based on the preset parameters, the particle clusters including a global particle cluster and a local particle cluster; performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle clusters, and updating the preset parameters, the individual best position and the global best position during the iterative calculation process; when it is detected that an iteration end condition is met, outputting the individual best position result, the global best position result and the key rate result corresponding to the global best position result according to the iterative calculation result.

[0008] In an exemplary embodiment of the present disclosure, before creating a particle swarm based on preset parameters of the PSO algorithm, the method further includes:

[0009] The preset parameters of the PSO algorithm are set, and the preset parameters include the number of particles in the particle swarm, the maximum number of iterative calculations, the initial inertia weight, the final inertia weight, the value range of the acceleration factor, and the preset ratio of the global search.

[0010] In an exemplary embodiment of the present disclosure, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process includes:

[0011] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0012] In determining the iterative calculation execution t After that, the inertia weight of the particle is updated according to the preset inertia weight algorithm, the maximum number of iterative calculations, the initial inertia weight, and the final inertia weight. The expression of the preset inertia weight algorithm includes:

[0013] ,

[0014] Among them, the Characterizes the inertia weight of the particle, the Characterizes the maximum number of iterative calculations, the Characterizes the initial inertia weight, the Characterize the final inertia weight.

[0015] In an exemplary embodiment of the present disclosure, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process further includes:

[0016] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0017] In determining the iterative calculation execution t After +1 times, according to the preset acceleration factor algorithm, the maximum number of iterative calculations and t The acceleration factor of the particle is updated by the acceleration factor of the second time, and the expression of the preset acceleration factor algorithm includes:

[0018] ,

[0019] Among them, the Characterization t +1 iteration calculation of the acceleration factor of the particle, the acceleration factor comprising a first acceleration factor corresponding to the individual optimal position and a second acceleration factor corresponding to the global optimal position, Characterizes the maximum number of iterative calculations, the Characterization t The acceleration factor of the particle calculated at the iteration.

[0020] In an exemplary embodiment of the present disclosure, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process further includes:

[0021] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0022] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The inertia weight updated after the iteration calculation, t The particle speed, the first acceleration factor, the first random number, and the t The individual optimal position of the particle calculated by the iteration, the second acceleration factor, the second random number, the t The global optimal position calculated by the iteration, t The position of the particle calculated by the iteration and the preset speed algorithm are used to update the speed of the particle. The expression of the preset speed algorithm includes:

[0023] ,

[0024] Among them, the Characterization t The inertia weight updated after the iteration calculation is Characterization t The particle velocity calculated in the iterations is Characterization t +1 iteration to calculate the particle velocity, Characterize the first acceleration factor, the Characterizes the first random number, the Characterization t The individual optimal position of the particle is calculated in the iterations. Characterize the second acceleration factor, the Characterizes the second random number, the Characterization t The global optimal position calculated by the iteration is Characterization t The position of the particle calculated at the iteration;

[0025] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the speed of the particle is randomly updated.

[0026] In an exemplary embodiment of the present disclosure, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process further includes:

[0027] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0028] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The particle speed calculated by the iteration t The position of the particle calculated in the iteration, t The position of the particle calculated by +1 iterations and the preset position algorithm are used to update the position of the particle. The expression of the preset position algorithm includes:

[0029] ,

[0030] Among them, the Characterization t +1 iteration to calculate the particle velocity, Characterization t The particle position calculated by the iteration is Characterization t +1 Iteration calculated particle position;

[0031] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the position of the particle is randomly updated.

[0032] In an exemplary embodiment of the present disclosure, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process includes:

[0033] Based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters are updated during the iterative calculation process;

[0034] After updating the preset parameters in the iterative calculation process, determining whether the particle satisfies a preset key rate constraint;

[0035] If it is determined that the particle satisfies the preset key rate constraint, the individual optimal position and the global optimal position of the particle are updated based on the updated preset parameters.

[0036] According to a second aspect of an embodiment of the present disclosure, a parameter optimization method for a QKD communication system is provided, including:

[0037] Obtaining preset parameters of a transmitting end of a QKD communication system and initializing the preset parameters;

[0038] Substituting the initialized preset parameters into the quantum key distribution parameter optimization method described in any of the above technical solutions for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation;

[0039] Substitute the optimal parameter solution into the objective function of the QKD communication system to obtain the optimal key rate solution of the QKD communication system.

[0040] In the above-mentioned embodiment of the present disclosure, a PSO strategy is adopted to generate multiple test points in the solution space, and the position update in the solution space is realized through the dynamic adjustment of individual advantages, group advantages and random factors. When the algorithm reaches the convergence state, all test points will be concentrated on the global optimal solution, that is, the best individual position. Based on this, it can be seen that the embodiment of the present disclosure does not need to preset the initial light intensity and light intensity probability, and can quickly and accurately optimize the parameters according to the configuration of the current system, thereby significantly improving the speed of parameter optimization.

[0041] Furthermore, the time cost of the present invention is significantly reduced compared to traditional local search algorithms, and it is easy to find the global optimal solution without falling into the local optimum, indicating that it has broad application potential in high-speed QKD systems and future large-scale quantum communication networks.

[0042] In an exemplary embodiment of the present disclosure, the expression of the objective function includes:

[0043] ,

[0044] Among them, the Characterizes the key rate, the max Characterize the optimal solution of the key rate, the Characterizing the count rate function of the Z-basis single-photon part of the QKD communication system, the Characterizing the phase error rate function of the Z basis, the The signal state representation is used to extract the count rate function of the optimal solution of the key rate, the The signal state characterizing the QKD communication system is used to extract the error rate of the optimal solution of the key rate, and the function Characterize the Shannon binary information entropy function, Characterize the error correction efficiency of the QKD communication system, the , , and stated The independent variables are the parameters of the QKD communication system.

[0045] In an exemplary embodiment of the present disclosure, the parameters of the QKD communication system include light intensity parameters and light intensity probabilities corresponding to the light intensity parameters, the light intensity parameters include a signal state, a decoy state, and a vacuum state, the light intensity probabilities corresponding to the light intensity parameters include the light intensity probability corresponding to the signal state, the light intensity probability corresponding to the decoy state, and the light intensity probability corresponding to the vacuum state, and the parameter optimization method of the QKD communication system further includes:

[0046] When performing the iterative calculation, the parameters of the QKD communication system are constrained based on preset parameter constraints, and the expressions of the parameter constraints include:

[0047] ,

[0048] ,

[0049] Among them, the μ Characterize the signal state, the ν Characterize the decoy state, the Characterize the vacuum state, the Characterize the light intensity probability corresponding to the signal state, the Characterize the light intensity probability corresponding to the trapped state and the Characterizes the light intensity probability corresponding to the vacuum state.

[0050] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0051] .

[0052] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0053] .

[0054] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0055] .

[0056] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0057] .

[0058] According to a third aspect of an embodiment of the present disclosure, a quantum key distribution parameter optimization device is provided, including:

[0059] The creation module is set to create particles based on the preset parameters of the PSO algorithm;

[0060] An initialization module, configured to initialize the individual best position and the global best position of any particle of the particle swarm to obtain initialization position information;

[0061] A division module, configured to divide the particle group into a plurality of particle clusters based on the preset parameters, wherein the particle clusters include a global particle cluster and a local particle cluster;

[0062] A calculation module, configured to perform iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and update the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process;

[0063] The output module is configured to output the individual best position result, the global best position result and the key rate result corresponding to the global best position result according to the iterative calculation result when it is detected that the iteration end condition is met.

[0064] According to a fourth aspect of an embodiment of the present disclosure, a parameter optimization device for a QKD communication system is provided, including:

[0065] An acquisition module, configured to acquire preset parameters of a transmitting end of a QKD communication system and initialize the preset parameters;

[0066] A calculation module is configured to substitute the initialized preset parameters into the quantum key distribution parameter optimization device as described in any of the above technical solutions for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation;

[0067] The calculation module is also configured to substitute the optimal parameter solution into the objective function of the QKD communication system to obtain the optimal key rate solution of the QKD communication system.

[0068] According to a fifth aspect of the present disclosure, there is provided an electronic device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the methods described above based on instructions stored in the memory.

[0069] According to a sixth aspect of the present disclosure, a computer-readable storage medium is provided, on which a program is stored, and the program is executed by a processor as described in any one of the methods described above.

[0070] According to a seventh aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program is executed by a processor as in any one of the methods described above.

[0071] Based on the embodiment of the present disclosure, particles are created based on preset parameters of the PSO algorithm, and the individual best position and the global best position of any particle of the particle group are initialized to obtain the initialization position information. Further, the particle group is divided into multiple particle clusters based on the preset parameters, and the particle cluster includes a global particle cluster and a local particle cluster. Then, based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters, the individual best position and the global best position are updated during the iterative calculation process. Finally, when it is detected that the iteration end condition is met, the individual best position result, the global best position result and the key rate result corresponding to the global best position result are output according to the iterative calculation result, so that the global optimal solution can be found quickly without falling into the local optimum like the local search algorithm (LSA), thereby improving the efficiency and reliability of the optimization parameters and saving a lot of computing resources.

[0072] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0074] Figure 1 is a schematic diagram of an exemplary system architecture to which the parameter optimization solution of an embodiment of the present invention can be applied;

[0075] Figure 2 is a flow chart of a method for optimizing quantum key distribution parameters in an exemplary embodiment of the present disclosure;

[0076] Figure 3 is a flow chart of another quantum key distribution parameter optimization method in an exemplary embodiment of the present disclosure;

[0077] Figure 4 is a flow chart of another quantum key distribution parameter optimization method in an exemplary embodiment of the present disclosure;

[0078] Figure 5 is a flow chart of a method for optimizing quantum key distribution parameters in an exemplary embodiment of the present disclosure;

[0079] Figure 6 is a flow chart of another quantum key distribution parameter optimization method in an exemplary embodiment of the present disclosure;

[0080] Figure 7 is a flow chart of another quantum key distribution parameter optimization method in an exemplary embodiment of the present disclosure;

[0081] Figure 8 is a flow chart of another quantum key distribution parameter optimization method in an exemplary embodiment of the present disclosure;

[0082] Fig. 9 is a flow chart of a parameter optimization method for a QKD communication system in an exemplary embodiment of the present disclosure;

[0083] Fig.10 is a flow chart of another parameter optimization method for a QKD communication system in an exemplary embodiment of the present disclosure;

[0084] Fig.11 is a hardware interaction schematic diagram of a parameter optimization scheme for a QKD communication system in an exemplary embodiment of the present disclosure;

[0085] Fig.12 is a block diagram of a quantum key distribution parameter optimization device in an exemplary embodiment of the present disclosure;

[0086] Fig.13 is a block diagram of a parameter optimization device for a QKD communication system in an exemplary embodiment of the present disclosure;

[0087] Fig.14 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0088] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0089] In addition, the accompanying drawings are only schematic diagrams of the present disclosure, and the same reference numerals in the drawings represent the same or similar parts, so their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0090] The embodiments of the present disclosure involve the following technical terms:

[0091] QKD: Quantum key distribution.

[0092] PSO: Particleswarm optimization, particle swarm optimization.

[0093] LSA: local search algorithm, local search algorithm.

[0094] Figure 1 A schematic diagram showing an exemplary system architecture to which the parameter optimization solution of an embodiment of the present invention can be applied is shown.

[0095] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0096] It should be understood that Figure 1The number of terminal devices, networks and servers is only illustrative. According to the implementation requirements, there can be any number of terminal devices, networks and servers. For example, the server 105 can be a server cluster composed of multiple servers.

[0097] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, etc.

[0098] In some embodiments, the parameter optimization method provided in the embodiments of the present invention is generally executed by the server 105, and accordingly, the parameter optimization device is generally set in the terminal device 103 (which may also be the terminal device 101 or 102). In other embodiments, some terminals may have functions similar to those of the server device to execute the method.

[0099] The exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0100] Figure 2 4 is a flow chart of a method for optimizing quantum key distribution parameters in an exemplary embodiment of the present disclosure.

[0101] refer to Figure 2 , the quantum key distribution parameter optimization method may include:

[0102] Step S202, creating particles based on preset parameters of the PSO algorithm;

[0103] Step S204, initializing the individual best position and the global best position of any particle of the particle swarm to obtain initialization position information;

[0104] Step S206, dividing the particle group into a plurality of particle clusters based on the preset parameters, the particle clusters including a global particle cluster and a local particle cluster;

[0105] Step S208, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process;

[0106] Step S210, when it is detected that the iteration end condition is met, outputting the individual best position result, the global best position result and the key rate result corresponding to the global best position result according to the iterative calculation result.

[0107] Based on the embodiment of the present disclosure, particles are created based on preset parameters of the PSO algorithm, and the individual best position and the global best position of any particle of the particle group are initialized to obtain the initialization position information. Further, the particle group is divided into multiple particle clusters based on the preset parameters, and the particle cluster includes a global particle cluster and a local particle cluster. Then, based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters, the individual best position and the global best position are updated during the iterative calculation process. Finally, when it is detected that the iteration end condition is met, the individual best position result, the global best position result and the key rate result corresponding to the global best position result are output according to the iterative calculation result, so that the global optimal solution can be found quickly without falling into the local optimum like the local search algorithm (LSA), thereby improving the efficiency and reliability of the optimization parameters and saving a lot of computing resources.

[0108] Below, each step of the quantum key distribution parameter optimization method is described in detail.

[0109] In an exemplary embodiment of the present disclosure, Figure 3 As shown, before creating a particle swarm based on the preset parameters of the PSO algorithm, it also includes:

[0110] Step S302, setting the preset parameters of the PSO algorithm, wherein the preset parameters include the number of particles in the particle swarm, the maximum number of iterative calculations, the initial inertia weight, the final inertia weight, the value range of the acceleration factor, and the preset ratio of the global search.

[0111] In an exemplary embodiment of the present disclosure, Figure 4 As shown, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process includes:

[0112] Step S402, performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0113] Step S404, determining that the iterative calculation is performed t After that, the inertia weight of the particle is updated according to the preset inertia weight algorithm, the maximum number of iterative calculations, the initial inertia weight, and the final inertia weight. The expression of the preset inertia weight algorithm includes:

[0114] ,

[0115] Among them, the Characterizes the inertia weight of the particle, the Characterizes the maximum number of iterative calculations, the Characterizes the initial inertia weight, the Characterize the final inertia weight.

[0116] In an exemplary embodiment of the present disclosure, Figure 5 As shown, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes:

[0117] Step S502, performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0118] Step S504, determining that the iterative calculation is performed t After +1 times, according to the preset acceleration factor algorithm, the maximum number of iterative calculations and t The acceleration factor of the particle is updated by the acceleration factor of the second time, and the expression of the preset acceleration factor algorithm includes:

[0119] ,

[0120] Among them, the Characterization t +1 iteration calculation of the acceleration factor of the particle, the acceleration factor comprising a first acceleration factor corresponding to the individual optimal position and a second acceleration factor corresponding to the global optimal position, Characterizes the maximum number of iterative calculations, the Characterization t The acceleration factor of the particle calculated at the iteration.

[0121] In an exemplary embodiment of the present disclosure, Figure 6 As shown, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes:

[0122] Step S602, performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0123] Step S604, after determining that the iterative calculation is performedt After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The inertia weight updated after the iteration calculation, t The particle speed, the first acceleration factor, the first random number, and the t The individual optimal position of the particle calculated by the iteration, the second acceleration factor, the second random number, the t The global optimal position calculated by the iteration, t The position of the particle calculated by the iteration and the preset speed algorithm are used to update the speed of the particle. The expression of the preset speed algorithm includes:

[0124] ,

[0125] Among them, the Characterization t The inertia weight updated after the iteration calculation is Characterization The particle velocity calculated in the iterations is Characterization t +1 iteration to calculate the particle velocity, Characterize the first acceleration factor, the Characterizes the first random number, the Characterization t The individual optimal position of the particle is calculated in the iterations. Characterize the second acceleration factor, the Characterizes the second random number, the Characterization Institute Description The global optimal position calculated by the iteration is Characterization t The position of the particle calculated at the iteration;

[0126] Step S606, after determining that the iterative calculation is performed t After +1 times, if it is determined that the particle belongs to the global particle cluster, the speed of the particle is randomly updated.

[0127] In an exemplary embodiment of the present disclosure, Figure 7 As shown, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes:

[0128] Step S702, performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0129] Step S704, determining that the iterative calculation is performed t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The particle speed calculated by the iteration t The position of the particle calculated in the iteration, t The position of the particle calculated by +1 iterations and the preset position algorithm are used to update the position of the particle. The expression of the preset position algorithm includes:

[0130] ,

[0131] Among them, the Characterization t +1 iteration to calculate the particle velocity, Characterization t The particle position calculated by the iteration is Characterization t +1 Iteration calculated particle position;

[0132] Step S706, after determining that the iterative calculation is performed t After +1 times, if it is determined that the particle belongs to the global particle cluster, the position of the particle is randomly updated.

[0133] In an exemplary embodiment of the present disclosure, Figure 8 As shown, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process includes:

[0134] Step S802, performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters during the iterative calculation process;

[0135] Step S804, after updating the preset parameters in the iterative calculation process, determining whether the particle satisfies a preset key rate constraint;

[0136] Step S806: If it is determined that the particle satisfies the preset key rate constraint, the individual optimal position and the global optimal position of the particle are updated based on the updated preset parameters.

[0137] Fig. 9 It is a flowchart of a parameter optimization method of a QKD communication system in an exemplary embodiment of the present disclosure.

[0138] like Fig. 9 As shown, the parameter optimization method of the QKD communication system may include:

[0139] Step S902, obtaining preset parameters of the transmitting end of the QKD communication system, and initializing the preset parameters;

[0140] Step S904, substituting the initialized preset parameters into the quantum key distribution parameter optimization method described in any of the above technical solutions for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation;

[0141] Step S906, substituting the optimal parameter solution into the objective function of the QKD communication system to obtain the optimal key rate solution of the QKD communication system.

[0142] In the above-mentioned embodiment of the present disclosure, a PSO strategy is adopted to generate multiple test points in the solution space, and the position update in the solution space is realized through the dynamic adjustment of individual advantages, group advantages and random factors. When the algorithm reaches the convergence state, all test points will be concentrated on the global optimal solution, that is, the best individual position. Based on this, it can be seen that the embodiment of the present disclosure does not need to preset the initial light intensity and light intensity probability, and can quickly and accurately optimize the parameters according to the configuration of the current system, thereby significantly improving the speed of parameter optimization.

[0143] Furthermore, the time cost of the present invention is significantly reduced compared to traditional local search algorithms, and it is easy to find the global optimal solution without falling into the local optimum, indicating that it has broad application potential in high-speed QKD systems and future large-scale quantum communication networks.

[0144] In an exemplary embodiment of the present disclosure, Fig.10 As shown, the quantum key distribution parameter optimization scheme based on the PSO algorithm includes the following steps:

[0145] Step S1002, initializing parameters.

[0146] Step S1004, initializing the particle swarm.

[0147] Step S1006, initializing the individual best position and the global best position.

[0148] Step S1008, the maximum number of iterations is reached or the change in the iteration result is less than the preset change. If so, execute step S1024, if not, execute step S1010.

[0149] Step S1010, updating the inertia weight and acceleration factor.

[0150] Step S1012, determine whether the particle belongs to the global particle set, if so, execute step S1014, if not, execute step S1016.

[0151] Step S1014, randomly update the position and speed.

[0152] Step S1016, updating the position and speed according to a preset algorithm.

[0153] Step S1018, whether it meets the preset key rate constraint, if so, execute step S1022, if not, execute step S1020.

[0154] Step S1020, locally adjust the particle position and speed.

[0155] Step S1022, updating the individual best position and the global best position.

[0156] Step S1024, output the global optimal position and key rate.

[0157] In an exemplary embodiment of the present disclosure, the expression of the objective function includes:

[0158] ,

[0159] Among them, the Characterize the key rate, the Characterize the optimal solution of the key rate, the Characterizing the count rate function of the Z-basis single-photon part of the QKD communication system, the Characterizing the phase error rate function of the Z basis, the The signal state representation is used to extract the count rate function of the optimal solution of the key rate, the The signal state characterizing the QKD communication system is used to extract the error rate of the optimal solution of the key rate, and the function Characterize the Shannon binary information entropy function, Characterize the error correction efficiency of the QKD communication system, the , , and stated The independent variables are the parameters of the QKD communication system.

[0160] In an exemplary embodiment of the present disclosure, the parameters of the QKD communication system include light intensity parameters and light intensity probabilities corresponding to the light intensity parameters, the light intensity parameters include a signal state, a decoy state, and a vacuum state, the light intensity probabilities corresponding to the light intensity parameters include the light intensity probability corresponding to the signal state, the light intensity probability corresponding to the decoy state, and the light intensity probability corresponding to the vacuum state, and the parameter optimization method of the QKD communication system further includes:

[0161] When performing the iterative calculation, the parameters of the QKD communication system are constrained based on preset parameter constraints, and the expressions of the parameter constraints include:

[0162] ,

[0163] ,

[0164] Among them, the Characterize the signal state, the Characterize the decoy state, the Characterize the vacuum state, the Characterize the light intensity probability corresponding to the signal state, the Characterize the light intensity probability corresponding to the trapped state and the Characterizes the light intensity probability corresponding to the vacuum state.

[0165] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0166] .

[0167] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0168] .

[0169] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0170] .

[0171] In an exemplary embodiment of the present disclosure, Fig.11 As shown, in the QKD communication system 1100, the receiver Charlie (which can be a central node) receives the communication signals from the sender Alice (which can be a user terminal) and the sender Bob (which can be a user terminal), and the distances between Alice and Charlie and Bob and Charlie are defined as L A and L B .

[0172] Alice, the sender, prepares a phase-randomized coherent state whose light intensity changes from the signal state , deception state and vacuum state The light intensity probabilities corresponding to the light intensities are randomly selected from , , .

[0173] The sender Bob prepares a phase-randomized coherent state whose light intensity changes from the signal state , deception state and vacuum state The light intensity probabilities corresponding to the light intensities are randomly selected from , , .

[0174] The objective function of parameter optimization is:

[0175]

[0176] in, is the key rate, is the count rate of the Z-basis single-photon part, is the phase error rate of the Z basis, The signal state is used to extract the count rate of the final key, is the error rate of the signal state used to extract the final key, is the Shannon binary information entropy function, f is the error correction efficiency. , , , are light intensity ( ) and the light intensity probability corresponding to the light intensity ( ) function.

[0177] Points that do not meet actual requirements must be excluded during optimization. Key rate constraints include but are not limited to:

[0178] ,

[0179] ,

[0180] ,

[0181] ,

[0182] ,

[0183] ,

[0184] ,

[0185] .

[0186] In an exemplary embodiment of the present disclosure, if L A and L BIf the intensity and light intensity probability of the light pulses sent by Alice and Bob are equal, it can be generally considered that they are the same. Therefore, only six parameters need to be optimized, which can be simplified as a symmetric channel situation.

[0187] In an exemplary embodiment of the present disclosure, if L A and L B If they are not equal, the intensity and light intensity probability of the optical pulses sent by Alice and Bob must be different. Here, the optimization parameters are 12, which can be simplified as the case of asymmetric channels.

[0188] In an exemplary embodiment of the present disclosure, the fewer the optimization parameters are, the faster the particle swarm optimization is and the shorter the optimization time is.

[0189] In an exemplary embodiment of the present disclosure, in actual QKD networking, asymmetric situations are the majority, and it is difficult to ensure that the distance from each user to the central node is the same.

[0190] In an exemplary embodiment of the present disclosure, the execution steps of the quantum key distribution parameter optimization scheme based on the particle swarm algorithm include:

[0191] (1) Initialization parameters include but are not limited to:

[0192] Set the number of particles N, the maximum number of iterations T, the initial value w_init and the final value w_final of the inertia weight w;

[0193] Set individual learning factor c1 and social learning factor c2;

[0194] Set the global exploration particle ratio h;

[0195] Set the fitness change threshold epsilon.

[0196] (2) Initialize the particle swarm. The initialization of each particle i includes but is not limited to:

[0197] Randomly initialize position X_i, including (light intensity, light intensity probability, etc.);

[0198] Make sure the initialization position satisfies the constraints;

[0199] Randomly initialize the velocity Vi.

[0200] (3) Initialization of individual and global optimal includes but is not limited to:

[0201] Calculate the fitness of each particle f(X_i) = R(…), R(…) is the function corresponding to max(R);

[0202] Initialize the individual best P_best_i of each particle as its initial position;

[0203] Find the global best position G_best.

[0204] In an exemplary embodiment of the present disclosure, the iterative optimization process can be implemented by the following code but is not limited thereto:

[0205] “for t from 1 to T:

[0206] Update w: w = w_init – ((w_init – w_final) / T) × t

[0207] For each particle i:

[0208] if I <N× h:

[0209] For global exploration particles, randomly update the position and velocity and ensure that the constraints are satisfied

[0210] else: Update speed and position according to the rules of the present invention:

[0211] V_id = w× V_id + c1× rand() × (P_best_id - X_id) + c2× rand()×(G_best_d - X_id)

[0212] X_id = X_id + V_id

[0213] Ensure that the updated position meets the constraints, and adjust if it does not

[0214] Update individual and global optima:

[0215] Calculate the fitness of the new position f(X_i)

[0216] If the new fitness is better, update the individual best P_best_i and the global best G_best

[0217] Record the current global best fitness f_new (G_best)

[0218] If abs(f_new(G_best) - f(G_best)) <epsilon:

[0219] Terminate iteration

[0220] Otherwise: Update the global best fitness f(G_best) = f_new(G_best)

[0221] Output result: Output the global best position G_best and the corresponding fitness value f(G_best)".

[0222] In an exemplary embodiment of the present disclosure, a QKD parameter optimization scheme based on the PSO algorithm may include:

[0223] S1.1. Set the parameters in the PSO algorithm, including the particle swarm size N , maximum number of iterations T , initial inertia weight and the final inertia weight , the range of acceleration factors includes and , the proportion of particles reserved for global exploration h .

[0224] S1.2. Create a particle group, each particle i The position in the D-dimensional search space can be expressed as , each particle i The speed in the D-dimensional search space can be expressed as .

[0225] S1.3. Initialization of two “best” positions for each particle: the individual best position of each particle Initialized to its initial position, the global optimal position Initialized to the best position of the individual with the best key rate in the entire particle population.

[0226] S2.1, t After rounds of iterations, the expressions for updating the inertia weight include: .

[0227] S2.2. The expression for updating the acceleration factor includes: , , which helps to focus more on global search in the early stages and more on local search in the later stages.

[0228] S2.3. For each particle i ,if , random update speed, if , otherwise the expression for the update speed includes: .

[0229] S2.4. For each particle i ,if , update the position randomly, otherwise the expressions for updating the position include: .

[0230] S2.5. The updated particles must satisfy the key rate constraint and remove the illegal points. Otherwise, the position will be adjusted to near the feasible value that meets the requirements.

[0231] S2.6. Update individual best position and the global optimal position .

[0232] S3, reaching the maximum number of iterations T , or when the global best position does not improve significantly in several consecutive iterations, the algorithm ends.

[0233] S4, return to the global best position and the key rate of the response .

[0234] In particular, the inertia weight in the above scheme It decreases linearly with the updating process, and can be replaced by exponential decrease, sinusoidal function decrease or logarithmic decrease according to actual conditions, but is not limited to these.

[0235] Corresponding to the above method embodiments, the present disclosure also provides a quantum key distribution parameter optimization device and a parameter optimization device for a QKD communication system, which can be used to execute the above method embodiments.

[0236] Fig.12 It is a block diagram of a quantum key distribution parameter optimization device in an exemplary embodiment of the present disclosure.

[0237] refer to Fig.12 , the quantum key distribution parameter optimization device 1200 may include:

[0238] A creation module 1202 is configured to create particles based on preset parameters of a PSO algorithm;

[0239] An initialization module 1204 is configured to initialize the individual best position and the global best position of any particle of the particle group to obtain initialization position information;

[0240] A division module 1206 is configured to divide the particle group into a plurality of particle clusters based on the preset parameters, wherein the particle clusters include a global particle cluster and a local particle cluster;

[0241] A calculation module 1208 is configured to perform iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and update the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process;

[0242] The output module 1210 is configured to output the individual best position result, the global best position result and the key rate result corresponding to the global best position result according to the iterative calculation result when it is detected that the iteration end condition is met.

[0243] In an exemplary embodiment of the present disclosure, before creating a particle swarm based on preset parameters of the PSO algorithm, the creation module 1202 is further configured to:

[0244] The preset parameters of the PSO algorithm are set, and the preset parameters include the number of particles in the particle swarm, the maximum number of iterative calculations, the initial inertia weight, the final inertia weight, the value range of the acceleration factor, and the preset ratio of the global search.

[0245] In an exemplary embodiment of the present disclosure, the calculation module 1208 is further configured to:

[0246] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0247] In determining the iterative calculation execution t After that, the inertia weight of the particle is updated according to the preset inertia weight algorithm, the maximum number of iterative calculations, the initial inertia weight, and the final inertia weight. The expression of the preset inertia weight algorithm includes:

[0248] ,

[0249] Among them, the Characterizes the inertia weight of the particle, the Characterizes the maximum number of iterative calculations, the Characterizes the initial inertia weight, the Characterize the final inertia weight.

[0250] In an exemplary embodiment of the present disclosure, the calculation module 1208 is further configured to:

[0251] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0252] In determining the iterative calculation execution t After +1 times, according to the preset acceleration factor algorithm, the maximum number of iterative calculations and t The acceleration factor of the particle is updated by the acceleration factor of the second time, and the expression of the preset acceleration factor algorithm includes:

[0253] ,

[0254] Among them, the Characterization t +1 iteration calculation of the acceleration factor of the particle, the acceleration factor comprising a first acceleration factor corresponding to the individual optimal position and a second acceleration factor corresponding to the global optimal position, Characterizes the maximum number of iterative calculations, the Characterization t The acceleration factor of the particle calculated at the iteration.

[0255] In an exemplary embodiment of the present disclosure, the calculation module 1208 is further configured to:

[0256] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0257] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The inertia weight updated after the iteration calculation, t The particle speed, the first acceleration factor, the first random number, and the t The individual optimal position of the particle calculated by the iteration, the second acceleration factor, the second random number, the t The global optimal position calculated by the iteration, t The position of the particle calculated by the iteration and the preset speed algorithm are used to update the speed of the particle. The expression of the preset speed algorithm includes:

[0258] ,

[0259] Among them, the Characterization t The inertia weight updated after the iteration calculation is Characterization t The particle velocity calculated in the iterations is Characterization t +1 iteration to calculate the particle velocity, Characterize the first acceleration factor, the Characterizes the first random number, the Characterization t The individual optimal position of the particle is calculated in the iterations. Characterize the second acceleration factor, the Characterizes the second random number, the Characterization t The global optimal position calculated by the iteration is Characterization t The position of the particle calculated at the iteration;

[0260] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the speed of the particle is randomly updated.

[0261] In an exemplary embodiment of the present disclosure, the calculation module 1208 is further configured to:

[0262] Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster;

[0263] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The particle speed calculated by the iteration t The position of the particle calculated in the iteration, t The position of the particle calculated by +1 iterations and the preset position algorithm are used to update the position of the particle. The expression of the preset position algorithm includes:

[0264] ,

[0265] Among them, the Characterization t +1 iteration to calculate the particle velocity, Characterization t The particle position calculated by the iteration is Characterization t +1 Iteration calculated particle position;

[0266] In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the position of the particle is randomly updated.

[0267] In an exemplary embodiment of the present disclosure, the calculation module 1208 is further configured to:

[0268] Based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters are updated during the iterative calculation process;

[0269] After updating the preset parameters in the iterative calculation process, determining whether the particle satisfies a preset key rate constraint;

[0270] If it is determined that the particle satisfies the preset key rate constraint, the individual optimal position and the global optimal position of the particle are updated based on the updated preset parameters.

[0271] Fig.13 It is a block diagram of a parameter optimization device of a QKD communication system in an exemplary embodiment of the present disclosure.

[0272] refer to Fig.13 , the parameter optimization device 1300 of the QKD communication system may include:

[0273] The acquisition module 1302 is configured to acquire preset parameters of a transmitting end of the QKD communication system and initialize the preset parameters;

[0274] The calculation module 1304 is configured to substitute the initialized preset parameters into the quantum key distribution parameter optimization device as described in any of the above technical solutions for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation;

[0275] The calculation module 1304 is also configured to substitute the optimal parameter solution into the objective function of the QKD communication system to obtain the optimal key rate solution of the QKD communication system.

[0276] In an exemplary embodiment of the present disclosure, the expression of the objective function includes:

[0277] ,

[0278] Among them, the Characterize the key rate, the Characterize the optimal solution of the key rate, the Characterizing the count rate function of the Z-basis single-photon part of the QKD communication system, the Characterizing the phase error rate function of the Z basis, the The signal state representation is used to extract the count rate function of the optimal solution of the key rate, the The signal state characterizing the QKD communication system is used to extract the error rate of the optimal solution of the key rate, and the function Characterize the Shannon binary information entropy function, Characterize the error correction efficiency of the QKD communication system, the , 、 Said and stated The independent variables are the parameters of the QKD communication system.

[0279] In an exemplary embodiment of the present disclosure, the parameters of the QKD communication system include light intensity parameters and light intensity probabilities corresponding to the light intensity parameters, the light intensity parameters include a signal state, a decoy state, and a vacuum state, the light intensity probabilities corresponding to the light intensity parameters include the light intensity probability corresponding to the signal state, the light intensity probability corresponding to the decoy state, and the light intensity probability corresponding to the vacuum state, and the parameter optimization device 1300 of the QKD communication system is further used to:

[0280] When performing the iterative calculation, the parameters of the QKD communication system are constrained based on preset parameter constraints, and the expressions of the parameter constraints include:

[0281] ,

[0282] ,

[0283] Among them, the Characterize the signal state, the Characterize the decoy state, the Characterize the vacuum state, the Characterize the light intensity probability corresponding to the signal state, the Characterize the light intensity probability corresponding to the trapped state and the Characterizes the light intensity probability corresponding to the vacuum state.

[0284] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0285] .

[0286] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0287] .

[0288] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0289] .

[0290] In an exemplary embodiment of the present disclosure, the expression of the parameter constraint condition further includes:

[0291] .

[0292] Since the functions of the quantum key distribution parameter optimization device 1200 and the parameter optimization device 1300 of the QKD communication system have been described in detail in their corresponding method embodiments, the present disclosure will not repeat them here.

[0293] In summary, compared with the prior art, the innovative features of the technical solution disclosed herein include but are not limited to:

[0294] (1) Improving the key rate and reducing the bit error rate: The PSO algorithm can effectively optimize the parameters of the QKD system (such as light intensity and light intensity probability) through global search and fast convergence, thereby maximizing the key rate, reducing the bit error rate, and improving the reliability of key transmission.

[0295] (2) Global search capability: The improved PSO algorithm enhances the global search capability, accelerates the convergence speed and optimization performance by reasonably selecting and adjusting parameters such as the proportion of particles retained for global exploration, updating the inertia weight and updating the acceleration factor. It can quickly and accurately optimize the maximum value of the key rate, improve the QKD coding rate and transmission distance under the same hardware configuration, and further reduce the deployment cost of QKD.

[0296] (3) Strong universality: It has the ability to be applied to various QKD protocols and can significantly improve the performance of the QKD system, thus providing strong support for the large-scale deployment and practical promotion of QKD.

[0297] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0298] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0299] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0300] Refer to the following Fig.14 14 to 15. The electronic device 1400 according to this embodiment of the present invention is described. Fig.14 The electronic device 1400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0301] like Fig.14As shown, the electronic device 1400 is in the form of a general computing device. The components of the electronic device 1400 may include but are not limited to: at least one processing unit 1410, at least one storage unit 1420, and a bus 1430 connecting different system components (including the storage unit 1420 and the processing unit 1410).

[0302] The storage unit stores program codes, which can be executed by the processing unit 1410, so that the processing unit 1410 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 1410 can perform the method shown in the embodiment of the present disclosure.

[0303] The storage unit 1420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 14201 and / or a cache storage unit 14202 , and may further include a read-only storage unit (ROM) 14203 .

[0304] The storage unit 1420 may also include a program / utility 14204 having a set (at least one) of program modules 14205, such program modules 14205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0305] Bus 1430 may be a local bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or any of a variety of bus architectures.

[0306] The electronic device 1400 may also communicate with one or more external devices 1440 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1400, and / or may communicate with any device that enables the electronic device 1400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1450. In addition, the electronic device 1400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1460. As shown, the network adapter 1460 communicates with other modules of the electronic device 1400 via a bus 1430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0307] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0308] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0309] The program product for implementing the above method according to an embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0310] Among them, the readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0311] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0312] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0313] In an exemplary embodiment of the present disclosure, a computer program product is also provided, which can be loaded or stored in any combination of one or more readable media, and can be written in any combination of one or more programming languages ​​to perform the program code of the present invention, wherein the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on the remote computing device, or completely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0314] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0315] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and concept of the present disclosure are indicated by the claims.

Claims

1. A method for optimizing quantum key distribution parameters, characterized in that: include: Create a particle swarm based on the preset parameters of the PSO algorithm; Initializing the individual best position and the global best position of any particle of the particle group to obtain initialization position information; Dividing the particle group into a plurality of particle clusters based on the preset parameters, the particle clusters including a global particle cluster and a local particle cluster; Based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters, the individual optimal position and the global optimal position are updated during the iterative calculation process, including: Based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters are updated during the iterative calculation process; After updating the preset parameters in the iterative calculation process, determining whether the particle satisfies a preset key rate constraint; If it is determined that the particle satisfies the preset key rate constraint, the individual best position and the global best position of the particle are updated based on the updated preset parameters; When it is detected that the iteration end condition is met, the individual best position result, the global best position result and the key rate result corresponding to the global best position result are output according to the iterative calculation result.

2. The method for optimizing quantum key distribution parameters according to claim 1, characterized in that: Before creating a particle swarm based on the preset parameters of the PSO algorithm, it also includes: The preset parameters of the PSO algorithm are set, and the preset parameters include the number of particles in the particle swarm, the maximum number of iterative calculations, the initial inertia weight, the final inertia weight, the value range of the acceleration factor, and the preset ratio of the global search.

3. The quantum key distribution parameter optimization method according to claim 2, characterized in that: Performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process includes: Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster; In determining the iterative calculation execution t After that, the inertia weight of the particle is updated according to the preset inertia weight algorithm, the maximum number of iterative calculations, the initial inertia weight, and the final inertia weight. The expression of the preset inertia weight algorithm includes: ， Among them, the Characterizes the inertia weight of the particle, the Characterizes the maximum number of iterative calculations, the Characterizes the initial inertia weight, the Characterize the final inertia weight.

4. The method for optimizing quantum key distribution parameters according to claim 2, characterized in that: Performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes: Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster; In determining the iterative calculation execution t After +1 times, according to the preset acceleration factor algorithm, the maximum number of iterative calculations and t The acceleration factor of the particle is updated by the acceleration factor of the second time, and the expression of the preset acceleration factor algorithm includes: ， Among them, the Characterization t +1 iteration calculation of the acceleration factor of the particle, the acceleration factor comprising a first acceleration factor corresponding to the individual optimal position and a second acceleration factor corresponding to the global optimal position, Characterizes the maximum number of iterative calculations, the Characterization t The acceleration factor of the particle calculated at the iteration.

5. The method for optimizing quantum key distribution parameters according to claim 2, characterized in that: Performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes: Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster; In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The inertia weight updated after the iteration calculation, t The particle speed, the first acceleration factor, the first random number, and the t The individual optimal position of the particle calculated by the iteration, the second acceleration factor, the second random number, the t The global optimal position calculated by the iteration, t The position of the particle calculated by the iteration and the preset speed algorithm are used to update the speed of the particle. The expression of the preset speed algorithm includes: ， Among them, the Characterization t The inertia weight updated after the iteration calculation is Characterization t The particle velocity calculated in the iterations is Characterization t +1 iteration to calculate the particle velocity, represents the first acceleration factor, the rand1 represents the first random number, the Characterization t The individual optimal position of the particle is calculated in the iterations. represents the second acceleration factor, the rand2 represents the second random number, the Characterization t The global optimal position calculated by the iteration is Characterization t The position of the particle calculated at the iteration; In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the speed of the particle is randomly updated.

6. The method for optimizing quantum key distribution parameters according to claim 2, characterized in that: Performing iterative calculation on the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and updating the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process also includes: Performing iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster; In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the local particle cluster, then according to the t The particle speed calculated in the iterations, t The position of the particle calculated by the iteration, t The position of the particle calculated by +1 iterations and the preset position algorithm are used to update the position of the particle. The expression of the preset position algorithm includes: ， Among them, the Characterization t +1 iteration to calculate the particle velocity, Characterization t The particle position calculated by the iteration is Characterization t +1 iteration calculated particle position; In determining the iterative calculation execution t After +1 times, if it is determined that the particle belongs to the global particle cluster, the position of the particle is randomly updated.

7. A parameter optimization method for a QKD communication system, characterized in that: include: Obtaining preset parameters of a transmitting end of a QKD communication system and initializing the preset parameters; Substituting the initialized preset parameters into the quantum key distribution parameter optimization method according to any one of claims 1 to 6 for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation; Substitute the optimal parameter solution into the objective function of the QKD communication system to obtain the optimal key rate solution of the QKD communication system. The expression of the objective function includes: ， Among them, the Characterize the key rate, the Characterize the optimal solution of the key rate, the Characterizing the count rate function of the Z-basis single-photon part of the QKD communication system, the Characterizing the phase error rate function of the Z basis, the The signal state representation is used to extract the count rate function of the optimal solution of the key rate, the The signal state characterizing the QKD communication system is used to extract the error rate of the optimal solution of the key rate, and the function Characterize the Shannon binary information entropy function, Characterize the error correction efficiency of the QKD communication system, the , , and stated The independent variables are the parameters of the QKD communication system.

8. The parameter optimization method of the QKD communication system according to claim 7, characterized in that: The parameters of the QKD communication system include light intensity parameters and light intensity probabilities corresponding to the light intensity parameters, the light intensity parameters include a signal state, a decoy state, and a vacuum state, the light intensity probabilities corresponding to the light intensity parameters include the light intensity probability corresponding to the signal state, the light intensity probability corresponding to the decoy state, and the light intensity probability corresponding to the vacuum state, and the parameter optimization method of the QKD communication system also includes: When performing the iterative calculation, the parameters of the QKD communication system are constrained based on preset parameter constraints, and the expressions of the parameter constraints include: ， ， Among them, the Characterize the signal state, the Characterize the decoy state, the Characterize the vacuum state, the Characterize the light intensity probability corresponding to the signal state, the Characterize the light intensity probability corresponding to the trapped state and the Characterizes the light intensity probability corresponding to the vacuum state.

9. The parameter optimization method of the QKD communication system according to claim 7, characterized in that: The expression of the parameter constraint condition also includes: 。 10. The parameter optimization method of the QKD communication system according to claim 7, characterized in that: The expression of the parameter constraint condition also includes: 。 11. The parameter optimization method of the QKD communication system according to claim 7, characterized in that: The expression of the parameter constraint condition also includes: 。 12. A quantum key distribution parameter optimization device, characterized in that: include: Create a module, set to create a particle swarm based on the preset parameters of the PSO algorithm; An initialization module, configured to initialize the individual best position and the global best position of any particle of the particle swarm to obtain initialization position information; A division module, configured to divide the particle group into a plurality of particle clusters based on the preset parameters, wherein the particle clusters include a global particle cluster and a local particle cluster; A calculation module is configured to perform iterative calculation of the position of the particle under the PSO algorithm based on the preset parameters, the initialization position information and the particle cluster, and update the preset parameters, the individual optimal position and the global optimal position during the iterative calculation process, including: Based on the preset parameters, the initialization position information and the particle cluster, the position of the particle is iteratively calculated under the PSO algorithm, and the preset parameters are updated during the iterative calculation process; After updating the preset parameters in the iterative calculation process, determining whether the particle satisfies a preset key rate constraint; If it is determined that the particle satisfies the preset key rate constraint, the individual best position and the global best position of the particle are updated based on the updated preset parameters; The output module is configured to output the individual best position result, the global best position result and the key rate result corresponding to the global best position result according to the iterative calculation result when it is detected that the iteration end condition is met.

13. A parameter optimization device for a QKD communication system, characterized in that: include: An acquisition module, configured to acquire preset parameters of a transmitting end of a QKD communication system and initialize the preset parameters; A calculation module, configured to substitute the initialized preset parameters into the quantum key distribution parameter optimization device according to any one of claims 1 to 6 for iterative calculation, so as to determine the optimal solution of the parameters of the QKD communication system according to the individual best position result, the global best position result and the key rate result corresponding to the global best position result of the iterative calculation; The calculation module is further configured to substitute the optimal solution of the parameters into the objective function of the QKD communication system to obtain the optimal solution of the key rate of the QKD communication system. The expression of the objective function includes: ， Among them, the Characterize the key rate, the Characterize the optimal solution of the key rate, the Characterizing the count rate function of the Z-basis single-photon part of the QKD communication system, the Characterizing the phase error rate function of the Z basis, the The signal state representation is used to extract the count rate function of the optimal solution of the key rate, the The signal state characterizing the QKD communication system is used to extract the error rate of the optimal solution of the key rate, and the function Characterize the Shannon binary information entropy function, Characterize the error correction efficiency of the QKD communication system, the , , and stated The independent variables are the parameters of the QKD communication system.

14. An electronic device, characterized in that: include: Memory; as well as A processor coupled to the memory, the processor being configured to execute, based on instructions stored in the memory, the quantum key distribution parameter optimization method as described in any one of claims 1-6 and / or the parameter optimization method for the QKD communication system as described in any one of claims 7-11.

15. A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the quantum key distribution parameter optimization method as described in any one of claims 1 to 6 and / or the parameter optimization method for the QKD communication system as described in any one of claims 7 to 11.

16. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the quantum key distribution parameter optimization method as described in any one of claims 1 to 6 and / or the parameter optimization method of the QKD communication system as described in any one of claims 7 to 11.

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