Satellite-to-ground continuous-variable quantum key distribution modulation variance optimization method and application

Abstract

The present invention discloses a satellite-to-ground continuous variable quantum key distribution modulation variance optimization method, comprising: obtaining communication data between a satellite and a ground receiving station; constructing a satellite-to-ground CV-QKD communication channel transmission model based on the communication data, simulating the transmission efficiency of the satellite-to-ground communication link and the transmittance variation intensity of the transmission channel; obtaining a key generation rate under the parameter configuration conditions based on the satellite-to-ground CV-QKD according to the simulation results, and calculating the system modulation variance; establishing a modulation variance optimization model based on the residual structure of a fully connected layer and inputting a training set; determining the error of the modulation variance optimization model based on a loss function, optimizing the modulation variance optimization model by backpropagating the error until the loss function converges to obtain the final modulation variance optimization model. The method can solve the problems of high computational complexity and high computing power requirements caused by optimizing the modulation variance parameters of the protocol using traditional local search algorithms or global search algorithms, and is suitable for satellite-to-ground CV-QKD communication systems.

Description

TECHNICAL FIELD

[0001] The application relates to the fields of quantum information processing and machine learning, and in particular to a modulation variance optimization method for satellite-ground continuous variable quantum key distribution based on artificial intelligence and application. BACKGROUND

[0002] Quantum Key Distribution (QKD) can distribute secure keys between two parties based on the security characteristics established under the framework of quantum mechanics and information theory, and can realize secure communication in the information theory sense by combining with the "one-time-one-key" encryption algorithm. Under the drive of the grand vision of the space-ground integrated quantum secure communication network, all-weather, high-stability and high-code-rate space quantum secure communication has become a research hotspot in the field of quantum information.

[0003] Quantum Key Distribution (QKD) can distribute secure keys between two parties based on the security characteristics established under the framework of quantum mechanics and information theory, and can realize secure communication in the information theory sense by combining with the "one-time-one-key" encryption algorithm. Under the drive of the grand vision of the space-ground integrated quantum secure communication network, all-weather, high-stability and high-code-rate space quantum secure communication has become a research hotspot in the field of quantum information. Quantum Key Distribution (QKD) can be divided into discrete variable quantum key distribution (DV-QKD) and continuous variable quantum key distribution (CV-QKD) according to the encoding mode of key information, and can be divided into optical fiber quantum key distribution and free space quantum key distribution according to the transmission medium of the channel. The Mozi quantum science experiment satellite has verified the feasibility of satellite-ground discrete variable quantum key distribution, entanglement distribution and teleportation experiment. The unmanned aerial vehicle quantum key distribution experiment has verified the feasibility of air-ground discrete variable quantum key distribution. However, the feasibility research of space CV-QKD is still in the stage of theoretical research and experimental preliminary exploration. Compared with the theoretical and experimental scheme of DV-QKD composed of single photon quantum state, polarization / phase / orbital angular momentum modulation, single photon detection, CV-QKD adopts the theoretical and experimental scheme of Gaussian / discrete modulation of coherent light quantum state, balanced homodyne / heterodyne detection and forward / backward processing, encodes key information on the canonical component of coherent light, and uses a homodyne or heterodyne detector to coherently measure the quantum state. CV-QKD has shown the ability to obtain higher secure code rate in the optical fiber channel, and has stronger compatibility with the existing optical communication system. Therefore, under the background of the gradual maturity of ground optical fiber QKD technology and the success of satellite-borne DV-QKD experiment, by learning from the theoretical and experimental development path of space DV-QKD, relying on the physical implementation technology basis such as classical laser communication link, acquisition tracking and pointing system, exploring the feasibility of theoretical and experimental research of space CV-QKD is an important part of the development of space-ground integrated quantum secure communication network. However, the satellite-ground CV-QKD communication system is faced with the problem of random noise caused by atmospheric turbulence, beam drift and channel loss and other actual channel disturbances. The above random noise changes rapidly with time in the communication process, leading to the attenuation and noise increase of the quantum state, affecting the security and efficiency of key generation, and real-time feedback and compensation are usually required in the actual process.

[0004] For the actual satellite-to-ground CV-QKD system, the modulation variance V a Optimal selection of the modulation variance is a critical step in achieving optimal system performance. Traditionally, optimizing the modulation variance relies on local or global search algorithms, which require high computing power. However, in practical applications of satellite-to-ground CV-QKD channels, these algorithms are computationally complex and slow on low-power platforms, making them difficult to apply to satellite-to-ground CV-QKD communication systems.

[0005] In recent years, the application of machine learning in quantum information has rapidly expanded. While traditional methods for optimizing modulation variance calculations are complex and time-consuming, machine learning algorithms can rapidly find optimized modulation variance parameters by learning from historical data, significantly reducing computation time. This efficient parameter optimization method is particularly important for CV-QKD systems that require low latency and low power consumption, particularly in satellite-to-ground CV-QKD communications. Summary of the Invention

[0006] In response to the above-mentioned defects or improvement needs of the existing technology, the present invention provides a satellite-to-ground continuous variable quantum key distribution modulation variance optimization method and application, which can solve the practical problems such as high computational complexity and high computing power requirements brought about by traditional local search algorithms or global search algorithms to optimize the protocol modulation variance parameters, and is suitable for satellite-to-ground CV-QKD communication systems.

[0007] On the one hand, an embodiment of the present invention provides a method for optimizing modulation variance of satellite-to-ground continuous variable quantum key distribution, which includes: obtaining communication data including satellite orbit altitude, real-time distance between satellite and ground stations, and satellite pitch angle within a common view time window between a satellite and a ground receiving station; constructing a satellite-to-ground CV-QKD communication channel transmission model based on the communication data, simulating the transmission efficiency of the satellite-to-ground communication link and the intensity of transmittance variation caused by random fluctuations in the transmission channel; obtaining a key generation rate under parameter configuration conditions based on satellite-to-ground CV-QKD according to the simulation results, and maximizing the key generation rate under a specified CV-QKD protocol according to the communication data. Under the condition of key generation rate, the modulation variance of the communication system after optimization is calculated; the modulation variance is used as a data set, the data set is enhanced and normalized, and divided into a training set and a test set; a modulation variance optimization model is established based on the residual structure of the fully connected layer, the training set is input into the modulation variance optimization model, and the mean square error loss function is determined as the loss function of the modulation variance optimization model; the error of the modulation variance optimization model is determined according to the loss function, and the error is back-propagated to optimize the modulation variance optimization model until the loss function converges to obtain the final modulation variance optimization model.

[0008] In an embodiment of the present application, the construction of the satellite-ground CV-QKD communication channel transmission model comprises: using an STK simulation platform to simulate satellite and ground station parameters, setting the semi-major axis, eccentricity, orbital inclination, argument of perigee, precision of ascending node, true anomaly of the simulation satellite and ground station, and the latitude and longitude coordinates and altitude of the simulation receiving ground station, to complete the simulation of the satellite-ground quantum key distribution satellite-ground physical link.

[0009] In an embodiment of the present application, the different dimension data of the data set are respectively normalized to scale the different dimension data to a standard range, and the data is divided into a training set and a test set, wherein, s4=-log 10 (ξ);wherein s1, s2, s3 and s4 represent the normalized return satellite orbital height, real-time satellite-ground station distance, satellite pitch angle data and communication channel excess noise data.

[0010] In an embodiment of the present application, the modulation variance optimization model comprises a first full connection layer, a first residual block, a second residual block, a third residual block, a fourth residual block, a fifth residual block, a sixth residual block, a seventh residual block, an eighth residual block and a second full connection layer arranged in series; wherein the first full connection layer is an input layer of the modulation variance optimization model, the input data dimension size is the communication data of the satellite-ground CV-QKD communication system under different communication states, and the second full connection layer module is an output layer of the modulation variance optimization model, and the output result is the optimized modulation variance of the satellite-ground CV-QKD communication system.

[0011] In an embodiment of the present application, the residual block structure comprises: a third full connection layer, a first batch normalization layer, a first linear rectifier function, a fourth full connection layer, a second batch normalization layer, a second linear rectifier function and an adder, and the input data of the residual block and the output data of the second batch normalization layer in the residual block constitute the input of the adder.

[0012] In an embodiment of the present application, the mean square error loss function is: wherein N represents the number of training sets, is the value predicted by the i th sample of the modulation variance optimization model, is the optimized modulation variance value of the i th sample by the local search algorithm.

[0013] In another aspect, the embodiment of the present application provides a satellite-to-ground continuous variable quantum key distribution modulation variance optimization device, which comprises: a communication data acquisition module, configured to acquire communication data of a satellite-to-ground station common view time window, the communication data comprising a satellite orbit height, a satellite-to-ground station real-time distance and a satellite pitch angle; a transmission model construction module, configured to construct a satellite-to-ground CV-QKD communication channel transmission model according to the communication data, simulate a transmission efficiency of a satellite-to-ground communication link and a variation intensity of a transmission channel caused by random fluctuations; a modulation variance calculation module, configured to obtain a key generation rate under a parameter configuration condition of a satellite-to-ground CV-QKD according to a simulation result, and calculate an optimized modulation variance of a communication system under a condition that a CV-QKD protocol is maximized at the key generation rate according to the communication data; a data processing module, configured to take the modulation variance as a data set, perform data enhancement and normalization processing on the data set, and divide the data set into a training set and a test set; a loss function determination module, configured to establish a modulation variance optimization model based on a full connection layer residual structure, input the training set into the modulation variance optimization model, and determine a mean square error loss function as a loss function of the modulation variance optimization model; and a model optimization module, configured to determine an error of the modulation variance optimization model according to the loss function, and perform back propagation on the error to optimize the modulation variance optimization model until a final modulation variance optimization model is obtained after the loss function converges.

[0014] In an embodiment of the present application, the transmission model construction module is specifically configured to: simulate satellite and ground station parameters by using an STK simulation platform, set a semi-major axis, an eccentricity, an orbit inclination, an argument of perigee, an ascending node precision, a true anomaly and a simulation receiving ground station latitude and longitude coordinates and an altitude of the simulation receiving ground station, and complete simulation of a satellite-to-ground quantum key distribution satellite-to-ground physical link.

[0015] In another aspect, the embodiment of the present application provides a satellite-to-ground continuous variable quantum key distribution modulation variance optimization system, which comprises: a memory and one or more processors connected to the memory, the memory storing a computer program, and the processor being configured to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to any one of the above embodiments.

[0016] In another aspect, the embodiment of the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are used to execute the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to any one of the above embodiments.

[0017] As can be seen from the above, compared with the prior art, the above-mentioned scheme conceived by the present application can have one or more of the following beneficial effects:

[0018] The modulation variance optimization method for satellite-ground CV-QKD based on machine learning can quickly optimize the key parameters of modulation variance, greatly shortening the calculation time. Compared with traditional algorithms, machine learning can learn the rules from a large amount of historical data and obtain parameters close to the optimal parameters in a short time. This efficient prediction capability can meet the real-time application requirements of the satellite-ground CV-QKD communication system. Secondly, the prediction method based on machine learning also performs excellently in accuracy. By training the model under different channel conditions, the machine learning algorithm can accurately capture complex environmental changes and adjust the system parameters accordingly, so that the CV-QKD can maintain high performance and stable key generation rate in a variable channel.

[0019] Other aspects of the application will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the application. It is to be understood that this purpose has been achieved regardless of the particular details of the figure, which is designed for explanatory purposes only and is not intended to limit the scope of the application. It should also be understood that the figures are not necessarily drawn to scale and that they merely seek to conceptually illustrate the structures and processes described herein. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings described herein are intended to provide further understanding of the application, form a part of the application, and the illustrative embodiments of the application and their descriptions serve to explain the application and do not constitute an improper limitation on the application. In the drawings:

[0021] Figure 1 A flow chart of a modulation variance optimization method for satellite-ground continuous variable quantum key distribution provided by an embodiment of the application;

[0022] Figure 2 An execution logic block diagram of a modulation variance optimization method for satellite-ground continuous variable quantum key distribution provided by an embodiment of the application;

[0023] Figure 3 A schematic diagram of a modulation variance optimization model provided by an embodiment of the application;

[0024] Figure 4 A schematic diagram of a residual block in a modulation variance optimization model provided by an embodiment of the application;

[0025] Figure 5 A structural schematic diagram of a modulation variance optimization device for satellite-ground continuous variable quantum key distribution provided by an embodiment of the application;

[0026] Figure 6 A structural schematic diagram of a modulation variance optimization system for satellite-ground continuous variable quantum key distribution provided by an embodiment of the application;

[0027] Figure 7 A structural schematic diagram of a computer readable storage medium provided by an embodiment of the application. DETAILED DESCRIPTION

[0028] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other in the case of no conflict. The present application will be described below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order for those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments of the present application, which should all belong to the protection scope of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are applicable to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the terms thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0031] It should also be noted that the division of the plurality of embodiments in the present application is only for the convenience of description, and should not constitute a special limitation. The features in the various embodiments can be combined with each other in the case of no conflict, and can be mutually quoted.

[0032]

First Embodiment

[0033] As Figure 1As shown, the first embodiment of the present application proposes a modulation variance optimization method for satellite-to-ground continuous variable quantum key distribution, comprising the following steps: step S1, obtaining communication data including satellite orbital height, real-time distance between satellite and ground station, and satellite pitch angle within the common view time window of satellite and ground receiving station; step S2, constructing a satellite-to-ground CV-QKD communication channel transmission model according to the communication data, simulating the transmission efficiency of satellite-to-ground communication link and the change intensity of transmittance caused by random fluctuations of the transmission channel; step S3, obtaining the key generation rate under the parameter configuration condition based on satellite-to-ground CV-QKD according to the simulation result, and calculating the optimized modulation variance of the communication system under the condition that the communication data maximizes the key generation rate of the specified CV-QKD protocol; step S4, taking the modulation variance as a data set, performing data enhancement and normalization processing on the data set, and dividing the data set into a training set and a test set; step S5, establishing a modulation variance optimization model based on the residual structure of the full connection layer, inputting the training set into the modulation variance optimization model, and determining the mean square error loss function as the loss function of the modulation variance optimization model; step S6, determining the error of the modulation variance optimization model according to the loss function, propagating the error back to optimize the modulation variance optimization model, until the final modulation variance optimization model is obtained after the loss function converges.

[0034] In combination Figure 2 As shown, in step S1, a satellite-to-ground station communication scenario is constructed. For example, the STK simulation platform is used to simulate satellite-to-ground station parameters to complete the simulation of the satellite-to-ground CV-QKD physical link. The STK simulation platform is used to simulate satellite-to-ground station parameters, by setting necessary parameters of the simulated satellite and ground station, including semi-major axis, eccentricity, orbital inclination, perihelion argument, ascending node precision, and true anomaly, as well as the latitude and longitude coordinates and altitude of the simulated receiving ground station, to complete the simulation of the satellite-to-ground quantum key distribution satellite physical link.

[0035] In step S2, the communication data is obtained by analyzing the change relationship between the distance between the satellite and the ground receiving station. The satellite orbital height, real-time distance between satellite and ground station, and satellite pitch angle data within the common view time window of satellite and ground receiving station are obtained through the STK simulation platform.

[0036] A satellite-ground CV-QKD communication channel transmission model is constructed. The establishment of a free space diffraction model is completed by MATLAB programming to calculate the free space diffraction effect. Subsequently, the establishment of an atmospheric extinction model is completed to simulate and evaluate the influence of atmospheric conditions on signal transmission. Finally, the establishment of an atmospheric turbulence model is completed. Through simulation of the phase and amplitude disturbance of the optical signal caused by different intensity atmospheric turbulence, the atmospheric turbulence loss under the real channel is calculated, and the transmission efficiency of the satellite-ground communication link and the intensity of the transmittance change caused by the random fluctuation of the transmission channel are simulated.

[0037] In step S3, the simulation key generation rate calculation is completed, for example, according to the satellite orbit height H returned by the STK, the satellite-ground communication implementation distance R, the satellite-ground elevation angle θ and the communication channel excess noise ξ data, through the satellite-ground CV-QKD protocol simulation calculation, the simulation of the satellite-ground quantum key distribution is completed, and the key generation rate under the parameter configuration condition based on the satellite-ground CV-QKD is obtained.

[0038] In step S4, the data set is generated. The modulation variance parameter V of the satellite-ground CV-QKD communication system is optimized by using a local search algorithm a , through the satellite orbit height H returned by the satellite, the real-time distance R of the satellite-ground station, the satellite elevation angle θ data and the communication channel excess noise ξ, the optimized modulation variance V of the communication system is calculated under the condition of maximizing the key generation rate of the specified CV-QKD protocol a .

[0039] Further, the data set is subjected to data enhancement processing. For example, the different dimension data of the data set are subjected to normalization processing respectively, so that the different dimension data are scaled to a standard range, and the data are divided into a training set and a test set, wherein, s4=-log 10 (ξ); wherein s1, s2, s3 and s4 represent the normalized satellite orbit height returned by the satellite, the real-time distance of the satellite-ground station, the satellite elevation angle data and the communication channel excess noise data.

[0040] In step S5, based on the residual network structure, a residual structure of a full connection layer is constructed to establish a modulation variance optimization model. For example, Figure 3As shown, the residual network structure established is based on the residual structure of the fully connected layer, and the modulation variance optimization model is established, including the first fully connected layer 1, the first residual block 2, the second residual block 3, the third residual block 4, the fourth residual block 5, the fifth residual block 6, the sixth residual block 7, the seventh residual block 8, the eighth residual block 9 and the second fully connected layer 10, which are arranged in series. Among them, the first fully connected layer module 1 is the input layer of the modulation variance optimization model, and the input data dimension size is the communication data under different communication states of the satellite-to-ground CV-QKD communication system. The second fully connected layer module 2 is the output layer of the modulation variance optimization model, and the output result is the optimized modulation variance V of the satellite-to-ground CV-QKD communication system. a value.

[0041] like Figure 4 As shown, the established modulation variance optimization model residual block, the first residual block 2 and the second residual block 3, the output is 64 dimensions; the third residual block 4 and the fourth residual block 5, the output is 128 dimensions; the fifth residual block 6 and the sixth residual block 7, the output is 256 dimensions and the seventh residual block 8 and the eighth residual block 9, the output is 512 dimensions. Among them, the residual blocks have the same structure, all including: a third fully connected layer 11, a first batch normalization layer 12, a first linear rectification function (ReLU function) 13, a fourth fully connected layer 14, a second batch normalization layer 15, a second linear rectification function 16 and an adder 17. The input data of the above residual blocks and the output data of the second batch normalization layer in the residual block constitute the input of the adder 17.

[0042] Furthermore, the mean square error loss function is determined as the loss function of the modulation variance optimization model. According to the output type of the modulation variance optimization model, the communication data returned by the satellite and the ground station are independent, so the mean square error (MSE) loss function can be used to determine the overall loss function of the modulation variance optimization model. MSE calculates the average value of the squared difference between the predicted value and the actual value, which makes its result very intuitive and easy to understand. Among them, the mean square error loss function is:

[0043]

[0044] Among them, N represents the number of training sets, is the value predicted by the modulated variance optimization model for the i-th sample, It is the modulation variance merit value optimized by the local search algorithm for the i-th sample, that is, the label value.

[0045] In step S6, the error of the modulation variance optimization model is determined according to a loss function, and an overall loss function Loss is obtained; the error of the modulation variance optimization model is back propagated, the parameters of the modulation variance optimization model are adjusted, and the modulation variance optimization model is optimized; the modulation variance optimization model is iteratively trained until the loss function converges, and the final modulation variance optimization model is obtained after the training is completed.

[0046] To test the performance of the machine learning-based satellite-ground continuous variable quantum key distribution modulation variance optimization method of the application, a sample set obtained by simulation with a satellite orbit range of 400 km to 800 km at intervals of 20 km is selected, including 126,575 sample quantities, wherein 90% of the data is used as a training set, containing 120247 data instances, and 10% of the training set is used as a test set, containing 6,328 data. The method of the application and the traditional modulation variance optimization algorithm (genetic algorithm) are compared in performance simulation test. The simulation experiment is performed on a Windows 11 system, the CPU model is 13 Gen Intel(R) Core(TM) i9-13900HX, the GPU model is Nvidia GeForce RTX 4060, the key generation efficiency of the CV-QKD system without switching protocol is simulated, and the experimental results are shown in Table 1. The experimental results show that the method of the application has a high accuracy in modulation variance optimization, the accuracy reaches 99.54%, and compared with the traditional calculation method, the time required for modulation variance optimization calculation is improved by 4 orders of magnitude.

[0047] Table 1 Comparison of performance of modulation variance optimization network and traditional algorithm

[0048]

[0049] In summary, the satellite-ground continuous variable quantum key distribution modulation variance optimization method proposed in the embodiments of the application can quickly optimize the modulation variance key parameters and greatly shorten the calculation time. Compared with the traditional algorithm, machine learning can learn the rules from a large amount of historical data and obtain parameters close to the optimal parameters in a short time. This efficient prediction capability can meet the real-time application requirements of the satellite-ground CV-QKD communication system. Secondly, the prediction method based on machine learning also performs excellently in accuracy. By training the model under different channel conditions, the machine learning algorithm can accurately capture complex environmental changes and adjust the system parameters accordingly, so that the CV-QKD maintains a high level of performance and stable key generation rate in a variable channel.

[0050]

Second Embodiment

[0051] As Figure 5As shown, the second embodiment of the present application proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization device 20, for example, comprising: a communication data acquisition module 201, a transmission model construction module 202, a modulation variance calculation module 203, a data processing module 204, a loss function determination module 205 and a model optimization module 206.

[0052] The communication data acquisition module 201 is configured to acquire communication data including satellite orbital altitude, real-time distance between satellite and ground station and satellite pitch angle within a common view time window of satellite and ground receiving station. The transmission model construction module 202 is configured to construct a satellite-to-ground CV-QKD communication channel transmission model according to the communication data, simulate the transmission efficiency of satellite-to-ground communication link and the change intensity of transmittance caused by random fluctuations of transmission channel. The modulation variance calculation module 203 is configured to obtain the key generation rate under the parameter configuration condition of satellite-to-ground CV-QKD according to the simulation result, and calculate the optimized modulation variance of the communication system under the condition that the key generation rate is maximized according to the communication data under the specified CV-QKD protocol. The data processing module 204 is configured to take the modulation variance as a data set, perform data enhancement and normalization processing on the data set, and divide the data set into a training set and a test set. The loss function determination module 205 is configured to establish a modulation variance optimization model based on a full connection layer residual structure, input the training set into the modulation variance optimization model, and determine the mean square error loss function as the loss function of the modulation variance optimization model. The model optimization module 206 is configured to determine the error of the modulation variance optimization model according to the loss function, and perform back propagation on the error to optimize the modulation variance optimization model until the loss function converges to obtain the final modulation variance optimization model.

[0053] Further, the transmission model construction module is specifically configured to simulate satellite and ground station parameters by using an STK simulation platform, set the semi-major axis, eccentricity, orbital inclination, perihelion argument, ascending node precision, true anomaly and simulation receiving ground station latitude and longitude coordinates and altitude of the simulation satellite and ground station, and complete the simulation of satellite-to-ground quantum key distribution satellite-to-ground physical link.

[0054] The method implemented by the satellite-to-ground continuous variable quantum key distribution modulation variance optimization device 20 disclosed in the second embodiment of the present application is as described in the foregoing first embodiment, and thus will not be described in detail here. Alternatively, each module in the second embodiment and the other operations or functions described above are respectively configured to implement the method described in the first embodiment, and the beneficial effects of the present embodiment are the same as those of the foregoing first embodiment. For the sake of brevity, they will not be described here.

[0055]

Third Embodiment

[0056] As Figure 6As shown, the third embodiment of the present invention proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization system 30, which includes, for example, a memory 32 and one or more processors 31 connected to the memory 32. The memory 32 stores a computer program, and the processor 31 is configured to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method described in the first embodiment. For details, please refer to the method described in the first embodiment and will not be repeated here for the sake of brevity. The beneficial effects of the satellite-to-ground continuous variable quantum key distribution modulation variance optimization system 30 provided in this embodiment are the same as the beneficial effects of the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method provided in the first embodiment.

[0057] [Fourth embodiment]

[0058] like Figure 7 As shown, the fourth embodiment of the present invention provides a computer-readable storage medium 40. The computer-readable storage medium 40 is a non-volatile memory and stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, for example, the one or more processors are caused to execute the method for optimizing the modulation variance of continuous variable quantum key distribution between satellites and ground stations described in the first embodiment. The specific method can be referred to the method described in the first embodiment and will not be described here for the sake of brevity. The beneficial effects of the computer-readable storage medium 40 provided in this embodiment are the same as those of the method for optimizing the modulation variance of continuous variable quantum key distribution between satellites and ground stations provided in the first embodiment.

[0059] In addition, it can be understood that the aforementioned embodiments are merely exemplary descriptions of the present invention. Under the premise that the technical features do not conflict, the structures do not contradict, and the purpose of the present invention is not violated, the technical solutions of the various embodiments can be arbitrarily combined and used in combination.

[0060] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and / or methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units / modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0061] The units / modules described as separate components may or may not be physically separate, and the components displayed as units / modules may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units / modules may be selected according to actual needs to achieve the purposes of the embodiments.

[0062] In addition, the functional units / modules in each embodiment of the present application can be integrated in one processing unit / module, or each unit / module can exist physically, or two or more units / modules can be integrated in one unit / module. The integrated unit / module can be realized in the form of hardware or in the form of hardware plus software functional unit / module.

[0063] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for optimizing modulation variance of satellite-to-ground continuous variable quantum key distribution, characterized in that: include: Acquire communication data including satellite orbit altitude, real-time distance between satellite and ground station, and satellite pitch angle within the common view time window between the satellite and ground receiving station; A satellite-to-ground CV-QKD communication channel transmission model is constructed based on the communication data to simulate the transmission efficiency of the satellite-to-ground communication link and the intensity of transmittance variation caused by random fluctuations in the transmission channel; Obtaining a key generation rate under the parameter configuration conditions of satellite-to-ground CV-QKD based on the simulation results, and calculating the optimized modulation variance of the communication system under the condition that the key generation rate is maximized under the specified CV-QKD protocol based on the communication data; The modulation variance is used as a data set, data enhancement and normalization are performed on the data set, and the data set is divided into a training set and a test set; Establishing a modulation variance optimization model based on the residual structure of the fully connected layer, inputting the training set into the modulation variance optimization model, and determining the mean square error loss function as the loss function of the modulation variance optimization model; The error of the modulation variance optimization model is determined according to the loss function, and the error is back-propagated to optimize the modulation variance optimization model until the loss function converges to obtain a final modulation variance optimization model.

2. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 1, characterized in that: The construction of the satellite-to-ground CV-QKD communication channel transmission model includes: The STK simulation platform is used to simulate the satellite and ground station parameters, set the semi-circumference, eccentricity, orbit inclination, pericenter argument, ascending node accuracy, true anomaly, and simulated receiving ground station latitude and longitude coordinates and altitude, and complete the simulation of the satellite-to-ground quantum key distribution satellite-to-ground physical link.

3. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 1, characterized in that: Normalize the different dimensional data of the dataset separately to scale the different dimensional data to the standard range, and divide the data into training set and test set. Among them, s1, s2, s3 and s4 represent the normalized return satellite orbit altitude, the real-time distance between the satellite and the ground station, the satellite pitch angle data and the communication channel excess noise data.

4. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 1, characterized in that: The modulation variance optimization model includes a first fully connected layer, a first residual block, a second residual block, a third residual block, a fourth residual block, a fifth residual block, a sixth residual block, a seventh residual block, an eighth residual block and a second fully connected layer, which are arranged in series in sequence; wherein, the first fully connected layer is the input layer of the modulation variance optimization model, and the input data dimension size is the communication data under different communication states of the satellite-to-ground CV-QKD communication system; the second fully connected layer module is the output layer of the modulation variance optimization model, and the output result is the optimized modulation variance of the satellite-to-ground CV-QKD communication system.

5. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 4, characterized in that: The residual block structure includes: a third fully connected layer, a first batch normalization layer, a first linear rectification function, a fourth fully connected layer, a second batch normalization layer, a second linear rectification function, and an adder, wherein the input data of the difference block and the output data of the second batch normalization layer in the residual block constitute the input of the adder.

6. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 1, characterized in that: The mean square error loss function is: Among them, N represents the number of training sets, is the value predicted by the modulated variance optimization model for the i-th sample, is the modulation variance merit of the i-th sample optimized by the local search algorithm.

7. A satellite-to-ground continuous variable quantum key distribution modulation variance optimization device, characterized in that: include: The communication data acquisition module is used to obtain communication data including satellite orbit altitude, real-time distance between satellite and ground station, and satellite pitch angle within the common view time window between the satellite and the ground receiving station; A transmission model construction module is used to construct a satellite-to-ground CV-QKD communication channel transmission model based on the communication data, and simulate the transmission efficiency of the satellite-to-ground communication link and the transmittance variation intensity caused by random fluctuations in the transmission channel; A modulation variance calculation module is used to obtain a key generation rate under the parameter configuration conditions of satellite-to-ground CV-QKD based on the simulation results, and calculate the optimized modulation variance of the communication system under the condition that the key generation rate is maximized under the specified CV-QKD protocol based on the communication data; A data processing module, configured to use the modulation variance as a data set, perform data enhancement and normalization on the data set, and divide the data set into a training set and a test set; A loss function determination module is used to establish a modulation variance optimization model based on the residual structure of the fully connected layer, input the training set into the modulation variance optimization model, and determine the mean square error loss function as the loss function of the modulation variance optimization model; The model optimization module is used to determine the error of the modulation variance optimization model according to the loss function, and back-propagate the error to optimize the modulation variance optimization model until the loss function converges to obtain the final modulation variance optimization model.

8. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization device according to claim 6, characterized in that: The transmission model construction module is specifically used for: The STK simulation platform is used to simulate the satellite and ground station parameters, set the semi-circumference, eccentricity, orbit inclination, pericenter argument, ascending node accuracy, true anomaly, and simulated receiving ground station latitude and longitude coordinates and altitude, and complete the simulation of the satellite-to-ground quantum key distribution satellite-to-ground physical link.

9. A satellite-to-ground continuous variable quantum key distribution modulation variance optimization system, characterized in that: include: A memory and one or more processors connected to the memory, the memory storing a computer program, and the processor being configured to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to any one of claims 1 to 6.

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