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

Abstract

The invention discloses a satellite-ground continuous variable quantum key distribution modulation variance optimization method. The method comprises the following steps: acquiring communication data of a satellite and a ground receiving station; constructing a satellite-ground CV-QKD communication channel transmission model according to the communication data, and simulating the transmission efficiency of a satellite-ground communication link and the transmittance change intensity of a transmission channel; obtaining a key generation rate under a parameter configuration condition based on satellite-ground CV-QKD according to a simulation result, and calculating a system modulation variance; establishing a modulation variance optimization model based on a residual structure of a full-connection layer and inputting a training set; and determining an error of the modulation variance optimization model according to the loss function, and carrying out back propagation on the error to optimize the modulation variance optimization model until the loss function is converged to obtain a final modulation variance optimization model. The method can solve the problems of high computational complexity, high computing power requirement and the like caused by optimizing protocol modulation variance parameters through a traditional local search algorithm or a global search algorithm, and is suitable for a satellite-ground CV-QKD communication system.

Description

Technical Field

[0001] The present invention relates to the field of quantum information processing and machine learning, and in particular to an artificial intelligence-based satellite-to-ground continuous variable quantum key distribution modulation variance optimization method and application. Background Art

[0002] Quantum Key Distribution (QKD) technology, with its security characteristics based on the framework of quantum mechanics and information theory, can distribute security keys between communicating parties. Combined with the "one-time one-pad" encryption algorithm, it can achieve secure and confidential communication in the sense of information theory. Driven by the grand vision of a space-ground integrated quantum secure communication network, all-weather, highly stable, and high-rate space quantum secure communication has become a research hotspot in the field of quantum information.

[0003] Quantum key distribution technology can be divided into discrete variable quantum key distribution (Discrete-Variable QKD, DV-QKD) and continuous variable quantum key distribution (Continuous-Variable QKD, CV-QKD) according to the key information encoding method, and can be divided into fiber quantum key distribution and free space quantum key distribution according to the channel transmission medium. The "Mozi" quantum science experimental satellite verified the feasibility of satellite-to-ground discrete variable quantum key distribution, entanglement distribution and teleportation experiments, and the drone quantum key distribution experiment verified the feasibility of air-to-ground discrete variable quantum key distribution. However, the feasibility study of space CV-QKD is still in the stage of theoretical research and preliminary experimental exploration. Compared with the theoretical and experimental scheme of DV-QKD, which uses single-photon quantum states, polarization / phase / orbital angular momentum modulation, and single-photon detection, CV-QKD uses the theoretical and experimental scheme of Gaussian / discrete modulation of coherent light quantum states, balanced zero-beat / homodyne detection, and forward / backward processing, encodes key information on the canonical component of coherent light, and uses homodyne or heterodyne detectors to perform coherent measurements on quantum states. In the optical fiber channel, CV-QKD has demonstrated the ability to obtain higher security code rates and is more compatible with existing optical communication systems. Therefore, in the context of the gradual maturity of ground-based optical fiber QKD technology and the success of satellite-borne DV-QKD experiments, it is an important piece of the puzzle to explore the feasibility of space CV-QKD in theory and experiment by drawing on the development path of space DV-QKD theory and experiment, relying on the physical realization technology foundations such as classical laser communication links and capture tracking and aiming systems, and exploring the feasibility of space CV-QKD in theory and experiment. However, the satellite-to-ground CV-QKD communication system faces the problem of random noise caused by actual channel disturbances such as atmospheric turbulence, beam drift and channel loss. The above random noise changes rapidly over time during the communication process, resulting in the attenuation of the quantum state and the increase of noise, affecting the security and efficiency of key generation. In actual processes, real-time feedback and compensation are usually required.

[0004] For the actual satellite-to-ground CV-QKD system, the modulation variance V a The optimal selection of is a key step to achieve the best system performance. Traditionally, the optimization of modulation variance relies on local search algorithms or global search algorithms, which require high computing power. However, in the actual application of satellite-to-ground CV-QKD channels, these algorithms are computationally complex and slow to run 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 the field of quantum information has expanded rapidly. When using traditional methods to optimize modulation variance calculations, the complexity is high and the time is long, while machine learning algorithms can quickly find the optimized modulation variance parameters by learning historical data, which greatly shortens the calculation time. Especially in the application scenario of satellite-to-ground CV-QKD communication, this efficient parameter optimization method is particularly important for CV-QKD systems that require low latency and low power consumption. Summary of the invention

[0006] In view of the above defects or improvement needs of the prior art, 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 of high computational complexity and high computing power requirements brought about by traditional local search algorithms or global search algorithms to optimize 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 height, 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 according to the communication data, simulating the transmission efficiency of the satellite-to-ground communication link and the intensity of transmittance variation caused by random fluctuations of 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 communication data under a specified CV-QKD protocol. 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 is 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 one embodiment of the present invention, the construction of the satellite-to-ground CV-QKD communication channel transmission model includes: using the STK simulation platform to simulate satellite and ground station parameters, setting the semi-circumference, eccentricity, orbital inclination, pericenter angle, ascending node accuracy, true anomaly angle of the simulated satellite and ground station, and simulating the longitude and latitude coordinates and altitude of the receiving ground station to complete the simulation of the satellite-to-ground physical link of satellite-to-ground quantum key distribution.

[0009] In one embodiment of the present invention, the different dimensional data of the data set are normalized respectively, so that the different dimensional 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); where 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.

[0010] In one embodiment of the present invention, 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 sequentially connected in series; 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.

[0011] In one embodiment of the present invention, 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, and 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.

[0012] In one embodiment of the present invention, the mean square error loss function is: Among them, N represents the number of training sets. is the value predicted by the i-th sample of the modulation variance optimization model, is the modulation variance merit optimized by the local search algorithm for the i-th sample.

[0013] On the other hand, an embodiment of the present invention proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization device, which includes: a communication data acquisition module, which is used to acquire 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; a transmission model construction module, which 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, which is used to obtain a key generation rate under parameter configuration conditions based on satellite-to-ground CV-QKD according to the simulation results, and maximize the key generation rate under a specified CV-QKD protocol according to the communication data. Under the condition of the key generation rate, the modulation variance of the communication system after optimization is calculated; a data processing module is used to use the modulation variance as a data set, perform data enhancement and normalization on the data set, and divide it 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; a 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.

[0014] In one embodiment of the present invention, the transmission model construction module is specifically used to: use the STK simulation platform to simulate the satellite and ground station parameters, set the semi-circumference, eccentricity, orbit inclination, periapsis angle, ascending node accuracy, true anomaly angle of the simulated satellite and ground station, and simulate the longitude and latitude coordinates and altitude of the receiving ground station to complete the simulation of the satellite-to-ground physical link of satellite-to-ground quantum key distribution.

[0015] On the other hand, an embodiment of the present invention proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization system, including: a memory and one or more processors connected to the memory, the memory storing a computer program, and the processor being used to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method as described in any one of the above embodiments.

[0016] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, wherein 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 as described in any one of the above embodiments.

[0017] It can be seen from the above that, compared with the prior art, the above solution conceived by the present invention can have one or more of the following beneficial effects:

[0018] The satellite-to-ground CV-QKD modulation variance optimization method based on machine learning proposed in the present invention can quickly optimize the key parameters of the modulation variance and greatly shorten the calculation time. Compared with traditional algorithms, machine learning can derive near-optimal parameters in a short time by learning rules from a large amount of historical data. This efficient prediction capability can meet the needs of real-time applications of satellite-to-ground CV-QKD communication systems. Secondly, the prediction method based on machine learning also performs well in accuracy. By training models under different channel conditions, the machine learning algorithm can accurately capture complex environmental changes and adjust system parameters accordingly, so that CV-QKD maintains a high level of performance and a stable key generation rate in a changing channel.

[0019] Other features of the present invention will become apparent from the following detailed description with reference to the accompanying drawings. It should be understood, however, that the drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. It should also be understood that, unless otherwise indicated, the drawings are not necessarily drawn to scale and are intended only to conceptually illustrate the structures and processes described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 A flowchart of a method for optimizing modulation variance of satellite-to-ground continuous variable quantum key distribution provided by an embodiment of the present invention;

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

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

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

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

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

[0027] Figure 7 A schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable ordinary technicians in the field to better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all should belong to the protection scope of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are applicable to distinguishing similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or are proprietary to these processes, methods, products or devices.

[0031] It should also be noted that the division of multiple embodiments in the present invention is only for the convenience of description and should not constitute a special limitation. The features in various embodiments can be combined and referenced to each other without contradiction.

[0032] [First embodiment]

[0033] like Figure 1As shown, the first embodiment of the present invention proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization method, comprising the following steps: step S1, obtaining communication data including satellite orbit height, satellite-to-ground station real-time distance and satellite pitch angle within the common view time window between the satellite and the 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 the satellite-to-ground communication link and the transmittance variation intensity caused by the random fluctuation of the transmission channel; step S3, obtaining the key generation rate under the parameter configuration conditions based on the satellite-to-ground CV-QKD according to the simulation results, and maximizing the key generation rate under the specified CV-QKD protocol according to the communication data. Under the condition of the key generation rate, the modulation variance of the communication system after optimization is calculated; step S4, taking the modulation variance as a data set, performing data enhancement and normalization processing on the data set, and dividing it into a training set and a test set; step S5, 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; step S6, determining the error of the modulation variance optimization model according to the loss function, back-propagating the error to optimize the modulation variance optimization model, until the loss function converges to obtain the final modulation variance optimization model.

[0034] Combination Figure 2 As shown, in step S1, a communication scenario between a satellite and a ground station is constructed. For example, the STK simulation platform is used to simulate the satellite and ground station parameters to complete the simulation of the satellite-to-ground CV-QKD physical link. The STK simulation platform is used to simulate the satellite and ground station parameters, and the necessary parameters of the simulated satellite and ground station are set, including: semi-circumference, eccentricity, orbital inclination, pericentric angle, ascending node accuracy and true anomaly, as well as the simulated receiving ground station latitude and longitude coordinates and altitude, to complete the simulation of the satellite-to-ground quantum key distribution satellite-to-ground physical link.

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

[0036] Construct a satellite-to-ground CV-QKD communication channel transmission model. Simulate and analyze the satellite-to-ground CV-QKD channel transmission model, complete the establishment of the free space diffraction model through MATLAB programming, and calculate the free space diffraction effect; then, complete the establishment of the atmospheric extinction model, simulate and evaluate the impact of atmospheric conditions on signal transmission; finally, complete the establishment of the atmospheric turbulence model. By simulating the phase and amplitude disturbances of the optical signal caused by atmospheric turbulence of different intensities in the atmosphere, calculate the atmospheric turbulence loss under the real channel, and complete the transmission efficiency of the satellite-to-ground communication link and the intensity simulation of the transmittance change caused by the random fluctuations of the transmission channel.

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

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

[0039] Furthermore, the data set is enhanced. For example, the different dimensional data of the data set are normalized separately to scale the different dimensional data to the standard range, and the data is divided into a training set and a test set. s4=-log 10 (ξ); where 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.

[0040] In step S5, based on the residual network structure, a residual structure of the fully connected layer is constructed to establish a modulation variance optimization model. 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 sequentially connected 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, and all include: 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] Further, 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 square 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 i-th sample of the modulation variance optimization model, It is the modulation variance merit 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 the loss function to obtain the overall loss function Loss; 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] In order to test the performance of the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method based on machine learning of the present invention, a sample set obtained by simulation at intervals of 20km with a satellite orbit range of 400km to 800km was selected, including 126,575 groups of samples, of which 90% of the data was used as a training set, including 120247 groups of data instances, and 10% of the training set was used as a test set, and the test set contained 6,328 groups of data. The method of the present invention is compared with the traditional modulation variance optimization algorithm (genetic algorithm) for performance comparison simulation test. The simulation experiment was carried out in the Windows 11 system, the CPU model was 13Gen Intel (R) Core (TM) i9-13900HX, and the GPU model was Nvidia GeForce RTX 4060, simulating the key generation efficiency of the CV-QKD system without a switch protocol, and the experimental results are shown in Table 1. The experimental results show that the method of the present invention has a high accuracy in modulation variance optimization, with an accuracy of 99.54%, and compared with the traditional calculation method, the time required for modulation variance optimization calculation is increased by 4 orders of magnitude.

[0047] Table 1 Performance comparison of modulation variance optimization network and traditional algorithm

[0048]

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

[0050] [Second embodiment]

[0051] like Figure 5As shown, the second embodiment of the present invention proposes a satellite-to-ground continuous variable quantum key distribution modulation variance optimization device 20, which includes: 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] Among them, the communication data acquisition module 201 is used to obtain communication data including satellite orbit height, real-time distance between satellite and ground stations and satellite pitch angle within the common view time window between the satellite and the ground receiving station. The transmission model construction module 202 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 change intensity caused by the random fluctuation of the transmission channel. The modulation variance calculation module 203 is used to obtain the key generation rate under the parameter configuration conditions of the satellite-to-ground CV-QKD according to 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 according to the communication data. The data processing module 204 is used to use the modulation variance as a data set, perform data enhancement and normalization on the data set, and divide it into a training set and a test set. The loss function determination module 205 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 206 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.

[0053] Furthermore, the transmission model construction module is specifically used to: use the STK simulation platform to simulate the satellite and ground station parameters, set the semi-circumference, eccentricity, orbit inclination, periapsis angle, ascending node accuracy, true anomaly angle of the simulated satellite and ground station, and simulate the longitude and latitude coordinates and altitude of the receiving ground station to complete the simulation of the satellite-to-ground physical link of satellite-to-ground quantum key distribution.

[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 invention is as described in the first embodiment, so it will not be described in detail here. Optionally, the various modules and the above-mentioned other operations or functions in the second embodiment are respectively for implementing the method described in the first embodiment, and the beneficial effects of this embodiment are the same as those of the first embodiment, and for the sake of brevity, they are not repeated here.

[0055] [Third embodiment]

[0056] like 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, for example, including: 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 used to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method as described in the first embodiment. The specific content can refer to the method described in the first embodiment, and will not be repeated here for the sake of brevity, and 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 proposes a computer-readable storage medium 40, which 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 satellite-to-ground continuous variable quantum key distribution modulation variance optimization method described in the first embodiment. The specific method can refer to the method described in the first embodiment, which will not be repeated here for the sake of brevity, and the beneficial effects of the computer-readable storage medium 40 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.

[0059] In addition, it can be understood that the aforementioned embodiments are only 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 only schematic. For example, the division of the units / modules is only a logical function division. There may be other division methods in actual implementation, 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 separated, and the components shown as units / modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units / modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional unit / module in each embodiment of the present invention may be integrated into one processing unit / module, or each unit / module may exist physically separately, or two or more units / modules may be integrated into one unit / module. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of hardware plus software functional units / modules.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

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 elevation angle within the common view time window between the satellite and ground receiving station; Constructing a satellite-to-ground CV-QKD communication channel transmission model based on the communication data, and simulating the transmission efficiency of the satellite-to-ground communication link and the intensity of transmittance variation caused by random fluctuations of 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 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 according to 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, periapsis angle, ascending node accuracy, true anomaly angle, as well as the longitude and latitude coordinates and altitude of the simulated receiving ground station, and complete the simulation of the satellite-to-ground physical link of satellite-to-ground quantum key distribution.

3. The satellite-to-ground continuous variable quantum key distribution modulation variance optimization method according to claim 1, characterized in that: The different dimensional data of the data set are normalized separately to scale the different dimensional data to the standard range, and the data are divided 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 dimension size of the input data 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 comprises: 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 i-th sample of the modulation variance optimization model, is the modulation variance merit optimized by the local search algorithm for the i-th sample.

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 acquire communication data including satellite orbit height, 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 building module is used to build a satellite-to-ground CV-QKD communication channel transmission model according to the communication data, and simulate the transmission efficiency of the satellite-to-ground communication link and the transmittance variation intensity caused by random fluctuations of 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 according to 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 according to the communication data; A data processing module, used for 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; A loss function determination module, 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 building 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, periapsis angle, ascending node accuracy, true anomaly angle, as well as the longitude and latitude coordinates and altitude of the simulated receiving ground station, and complete the simulation of the satellite-to-ground physical link of satellite-to-ground quantum key distribution.

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 used to execute the computer program to implement the satellite-to-ground continuous variable quantum key distribution modulation variance optimization method as described in 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 as described in any one of claims 1 to 6.

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