Parameter estimation process optimization method and device suitable for CV-QKD system

Abstract

The application discloses a parameter estimation process optimization method and device suitable for a CV-QKD system, belongs to the field of quantum key distribution, and comprises the following steps: S1, original data acquisition; S2, parameter estimation data preparation; S3, data transmission; and S4, parameter estimation calculation. The application optimizes the parameter estimation process architecture, reduces transmission waiting in the running process, improves the parameter estimation throughput without increasing information leakage, and provides support for improving the data post-processing performance of an actual CV-QKD system. Furthermore, compared with the known public scheme, the parameter estimation throughput of the CV-QKD system can be further improved, and the scheme is suitable for homodyne and heterodyne detection schemes.

Description

Technical Field

[0001] This invention relates to the field of quantum key distribution, and more specifically, to a method and apparatus for optimizing the parameter estimation process of CV-QKD systems. Background Technology

[0002] Existing quantum key distribution (QKD) technologies mainly fall into two categories: discrete variable quantum key distribution (DV-QKD) and continuous variable quantum key distribution (CV-QKD). Compared to DV-QKD, CV-QKD does not require a single-photon source or detector; most of its components are compatible with classical coherent optical communication, exhibiting good compatibility with traditional optical communication networks. It also possesses the potential for high repetition rates and high key rates, offering significant advantages in cost and performance, making it suitable for secure communication at critical nodes in metropolitan areas.

[0003] The workflow of a CV-QKD system mainly includes quantum signal transmission and key post-processing. The quantum state transmission process enables the two communicating parties to obtain related but not identical original data. The data post-processing process, based on the original data, uses steps such as basis comparison (optional), parameter estimation, data negotiation, error correction decoding, and private key amplification to enable the two communicating parties to obtain a consistent security key.

[0004] The rapid increase in repetition frequency in CV-QKD systems poses a significant challenge to the throughput of data post-processing. The main factors affecting post-processing throughput include post-processing algorithm design and post-processing workflow architecture design. For the parameter estimation process, the calculation steps are numerous, typically completed at either the sending or receiving end, and communication latency occurs during the calculation, increasing the overall time consumption. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and, in response to the high throughput requirements of parameter estimation calculation in the post-processing of data in actual CV-QKD systems, to provide a parameter estimation process optimization method and apparatus suitable for CV-QKD systems. By optimizing the parameter estimation process architecture, the transmission waiting time during operation is reduced, and the parameter estimation throughput is improved without increasing information leakage, thus providing key support for improving the performance of CV-QKD system data post-processing.

[0006] The objective of this invention is achieved through the following solution:

[0007] An optimization method for parameter estimation process applicable to CV-QKD systems includes the following steps:

[0008] S1, raw data acquisition: Alice obtains the raw Gaussian random number x from the post-processing basis comparison process, and Bob obtains the raw Gaussian random number y from the post-processing basis selection process; Alice and Bob obtain the same 0 / 1 random number paraChoice, which is used to randomly filter data for parameter estimation and data negotiation.

[0009] S2, parameter estimation data preparation: x and y are selected for parameter estimation (xpara, ypara) and data negotiation (xmulti, ymulti) using paraChoice; at the same time, Bob calculates the variance of ypara (vary).

[0010] S3, data transmission: Bob sends the calculated ypara and variance versus to Alice for parameter estimation.

[0011] S4, parameter estimation calculation: Alice uses the obtained xpara, ypara, and vary to perform parameter estimation calculation, thus obtaining the parameter estimation result.

[0012] Furthermore, in step S1, Alice and Bob obtain consistent 0 / 1 random numbers paraChoice and Alice obtain the original Gaussian random number x from the post-processing basis comparison process, or Bob obtains the original Gaussian random number y from the post-processing basis selection process, simultaneously.

[0013] Furthermore, in step S1, the data negotiation includes reverse data negotiation.

[0014] Furthermore, in step S1, the random number paraChoice is obtained through a random number generator.

[0015] Further, in step S1, the random number paraChoice is obtained from Bob's end and sent to Alice's end.

[0016] Furthermore, in step S1, the random number paraChoice is read from random numbers pre-stored at the Alice end and the Bob end.

[0017] Furthermore, the random numbers pre-stored on the Alice and Bob ends are updated periodically.

[0018] A parameter estimation process optimization device suitable for CV-QKD systems includes: a random number generation module, a parameter estimation data preparation module, and a parameter estimation data calculation module;

[0019] The random number generation module is used to generate 0 / 1 random numbers paraChoice;

[0020] The parameter estimation data preparation module includes parameter estimation data preparation module A and parameter estimation data preparation module B. Parameter estimation data preparation module A is used by Alice to obtain a Gaussian random number x from the basis comparison module, obtain a 0 / 1 random number paraChoice from the random number generation module, filter x using paraChoice to obtain data for parameter estimation (xpara) and data negotiation (xmulti), and send xpara to the parameter estimation data calculation module. Parameter estimation data preparation module B is used by Bob to obtain a Gaussian random number y from the basis selection module, obtain a 0 / 1 random number paraChoice consistent with that in parameter estimation data preparation module A from the random number generation module, filter y using paraChoice to obtain data for parameter estimation (ypara) and data negotiation (ymulti), and calculate the variance vs. using the filtered ypara.

[0021] The parameter estimation data calculation module is used to complete the parameter estimation calculation process and calculate the security code rate.

[0022] Furthermore, the random number paraChoice is obtained through a random number generator.

[0023] Furthermore, the random number paraChoice is obtained from Bob's end and sent to Alice's end, or it is read from a consistent random number pre-stored at both ends, and the pre-stored consistent random number is updated periodically.

[0024] The beneficial effects of this invention include:

[0025] This invention proposes a parameter estimation process optimization method and apparatus suitable for CV-QKD systems. By optimizing the parameter estimation process architecture, the transmission waiting time during operation is reduced, and the parameter estimation throughput is improved without increasing information leakage, thus providing support for improving the data post-processing performance of actual CV-QKD systems.

[0026] Compared with known public solutions, the present invention can further improve the throughput of parameter estimation in CV-QKD systems, and the present invention is applicable to both zero-difference and heterodyne detection schemes. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 These are the implementation steps of an embodiment of the present invention;

[0029] Figure 2 This is an implementation apparatus for an embodiment of the present invention. Detailed Implementation

[0030] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0031] In view of the current situation, this invention conceives a parameter estimation process optimization solution for CV-QKD systems, aiming to optimize the parameter estimation process and rationally allocate parameter estimation computational resources. Specifically, this invention focuses on the parameter estimation step in the data post-processing stage, proposing an optimization method and apparatus for the parameter estimation process architecture suitable for CV-QKD systems. By optimizing the parameter estimation process architecture, the throughput of parameter estimation is improved without increasing information leakage, thereby improving the overall throughput of CV-QKD data post-processing.

[0032] The specific implementation steps of the method are as follows: Figure 1 As shown, the device diagram is as follows. Figure 2 As shown, the application scenarios of this method and device include, but are not limited to, reverse data negotiation.

[0033] (a) such as Figure 1 As shown, this embodiment takes reverse data negotiation under the GG02 protocol as an example. The specific implementation steps of the method in this embodiment are as follows:

[0034] Step 1: Raw Data Acquisition. First, raw Gaussian random numbers are obtained from the previous step (basis alignment / basis selection) of post-processing parameter estimation. Alice obtains raw Gaussian random number x from the basis alignment step of post-processing, and Bob obtains raw Gaussian random number y from the basis selection step of post-processing. At the same time, Alice and Bob obtain the same 0 / 1 random number paraChoice, which is used to randomly filter the data for parameter estimation and data negotiation. This random number can be obtained through a random number generator, either obtained from Bob and sent to Alice, or read from the random numbers pre-stored at both ends.

[0035] Step 2: Parameter estimation data preparation. Using paraChoice, x and y are selected for parameter estimation (xpara, ypara) and data negotiation (xmulti, ymulti). Simultaneously, Bob calculates the variance (vary) of ypara.

[0036] Step 3: Data transmission. Bob sends the calculated ypara and variance versus to Alice for parameter estimation.

[0037] Step 4: Parameter estimation calculation. Alice uses the obtained xpara, ypara, and vary for parameter estimation calculation to obtain the parameter estimation result. In the technical concept of this embodiment, the parameter estimation process mainly consumes time in calculating the intermediate parameters ∑xpara*xpara, ∑xpara*ypara, and the variances of xpara (varx) and ypara (vary), with the calculations of varx and vary being the most time-consuming. The vary calculation is performed on Bob's end. When Bob sends ypara, vary is also sent to Alice's end. Since only one parameter (vary) is transmitted, it does not significantly increase transmission time. Simultaneously, during the reverse data negotiation process, Bob's post-processing involves less computation; it primarily waits for Alice's calculations to complete and for data to be transmitted or received. The vary calculation can be completed during Bob's idle time. This operation, compared to performing all parameter estimation calculations directly on Alice's end, reduces the parameter estimation calculation time by at least one-third.

[0038] (ii) Figure 2 The diagram shows the system implementation device.

[0039] The system implementation device is divided into a parameter estimation data preparation module, a random number generation module, and a parameter calculation module.

[0040] (1) Random number generation module

[0041] The random number generation module is used to generate 0 / 1 random numbers paraChoice. These random numbers can be obtained through a random number generator, either from Bob's end and sent to Alice's end, or read from consistent random numbers pre-stored at both ends (which need to be updated periodically).

[0042] (2) Parameter estimation data preparation module A and parameter estimation data preparation module B

[0043] The parameter estimation data preparation module A is used to obtain a Gaussian random number x from the base comparison module on the Alice side, obtain a 0 / 1 random number paraChoice through the random number generation module, filter x through paraChoice to obtain data for parameter estimation (xpara) and data negotiation (xmulti), and send xpara to the parameter estimation data calculation module.

[0044] The parameter estimation data preparation module B is used to obtain a Gaussian random number y from the basis selection module on the Bob side. It obtains a 0 / 1 random number paraChoice consistent with the parameter estimation data preparation module A from the random number generation module. The y is filtered by paraChoice to obtain data for parameter estimation (ypara) and data negotiation (ymulti). The variance vsy is calculated using the filtered ypara.

[0045] (3) Parameter estimation data calculation module

[0046] The parameter estimation calculation module is used to complete the parameter estimation calculation process and calculate the secure bitrate. Its main time consumption is in the calculation of ∑xpara*xpara, ∑xpara*ypara, the variance varx of xpara, and other simple parameter estimation calculations. The time consumption is very small and can be ignored. For the specific calculation implementation process, please refer to the implementation case below.

[0047] To further illustrate the present invention, in other embodiments, such as the parameter estimation process of the present invention under the Gaussian modulation GG02 protocol, the specific content includes the following: Assuming the data length used for parameter estimation is N, the parameter estimation calculation process is as follows:

[0048] (1) First, calculate the channel transmittance t and transmittance T, where η is the detection efficiency, and the time for calculating t is recorded as time1;

[0049]

[0050]

[0051] (2) Next, calculate the variance of xpara and ypara. The time record for calculating xpara is time2, and the time record for calculating ypara is time3.

[0052]

[0053]

[0054] (3) Finally, using the parameters t, T, varx, and vary mentioned above, other parameters required for parameter estimation are calculated. The calculation process is a simple single-parameter calculation process without involving loop calculations. The time taken is recorded as time4, where varz is the noise variance, ξ is the over-noise, and χ is the noise variance. line , χ hom , χ tot These are the channel equivalent input noise, the equivalent input noise introduced by the zero-difference detector, and the total equivalent input noise between Alice and Bob, respectively. AB and χ BE The mutual information between Alice and Bob, and between Bob and Eve, represents the parameters used to calculate the secure code rate. The remaining parameters are intermediate parameters. Where G(x) = (x+1)log2(x+1) - xlog2x.

[0055] varz = vary-t 2 varx

[0056] ξ=(var z-1-v ele ) / t 2 ;

[0057] V = varx + 1

[0058]

[0059]

[0060] A = V 2 (1-2T)+2T+T 2 (V+χ line ) 2

[0061] B = T 2 (Vχ line +1) 2

[0062]

[0063]

[0064]

[0065]

[0066] Taking the following test platform as an example, the CPU model is AMD EPYC 7452 (32 cores, 2.35GHz), and the amount of data N to be calculated is 576*10. 6The data volume was measured, and the test results were: time1 = 0.259s, time2 = 0.250s, time3 = 0.266s, and time4 = 0s. Before optimization, time0 = time1 + time2 + time3 + time4 = 0.775s. After optimization, the computation time was time1 + time2 + time4 = 0.509s, and the throughput increased from 743Mbps to 1131Mbps. The test results show that the proposed solution effectively improves the parameter estimation throughput without increasing information leakage, providing support for improving the data post-processing performance of actual CV-QKD systems.

[0067] It should be noted that, within the scope of protection defined in the claims of this invention, the following embodiments can be combined and / or extended or replaced in any logical manner from the above specific embodiments, such as the disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0068] Example 1

[0069] An optimization method for parameter estimation process applicable to CV-QKD systems includes the following steps:

[0070] S1, raw data acquisition: Alice obtains the raw Gaussian random number x from the post-processing basis comparison process, and Bob obtains the raw Gaussian random number y from the post-processing basis selection process; Alice and Bob obtain the same 0 / 1 random number paraChoice, which is used to randomly filter data for parameter estimation and data negotiation.

[0071] S2, parameter estimation data preparation: x and y are selected for parameter estimation (xpara, ypara) and data negotiation (xmulti, ymulti) using paraChoice; at the same time, Bob calculates the variance of ypara (vary).

[0072] S3, data transmission: Bob sends the calculated ypara and variance versus to Alice for parameter estimation.

[0073] S4, parameter estimation calculation: Alice uses the obtained xpara, ypara, and vary to perform parameter estimation calculation, thus obtaining the parameter estimation result.

[0074] Example 2

[0075] Based on Example 1, in step S1, Alice and Bob obtain consistent 0 / 1 random numbers paraChoice and Alice obtain the original Gaussian random number x from the post-processing basis comparison process, or Bob obtains the original Gaussian random number y from the post-processing basis selection process, simultaneously.

[0076] Example 3

[0077] Based on Example 1, in step S1, the data negotiation includes reverse data negotiation.

[0078] Example 4

[0079] Based on Example 1, in step S1, the random number paraChoice is obtained through a random number generator.

[0080] Example 5

[0081] Based on Example 1, in step S1, the random number paraChoice is obtained from Bob's end and sent to Alice's end.

[0082] Example 6

[0083] Based on Example 1, in step S1, the random number paraChoice is read from the random numbers pre-stored at Alice's end and Bob's end.

[0084] Example 7

[0085] Based on Example 6, the random numbers pre-stored on the Alice end and the Bob end are updated periodically.

[0086] Example 8

[0087] A parameter estimation process optimization device suitable for CV-QKD systems includes: a random number generation module, a parameter estimation data preparation module, and a parameter estimation data calculation module;

[0088] The random number generation module is used to generate 0 / 1 random numbers paraChoice;

[0089] The parameter estimation data preparation module includes parameter estimation data preparation module A and parameter estimation data preparation module B. Parameter estimation data preparation module A is used by Alice to obtain a Gaussian random number x from the basis comparison module, obtain a 0 / 1 random number paraChoice from the random number generation module, filter x using paraChoice to obtain data for parameter estimation (xpara) and data negotiation (xmulti), and send xpara to the parameter estimation data calculation module. Parameter estimation data preparation module B is used by Bob to obtain a Gaussian random number y from the basis selection module, obtain a 0 / 1 random number paraChoice consistent with that in parameter estimation data preparation module A from the random number generation module, filter y using paraChoice to obtain data for parameter estimation (ypara) and data negotiation (ymulti), and calculate the variance vs. using the filtered ypara.

[0090] The parameter estimation data calculation module is used to complete the parameter estimation calculation process and calculate the security code rate.

[0091] Example 9

[0092] Based on Example 8, the random number paraChoice is obtained through a random number generator.

[0093] Example 10

[0094] Based on Example 8, the random number paraChoice is obtained from Bob's end and sent to Alice's end, or it is read from a consistent random number pre-stored at both ends, and the pre-stored consistent random number is updated periodically.

[0095] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0096] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0097] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. A parameter estimation process optimization method suitable for CV-QKD systems, characterized in that, Includes the following steps: S1, raw data acquisition: Alice obtains the raw Gaussian random number x from the post-processing basis comparison process, and Bob obtains the raw Gaussian random number y from the post-processing basis selection process; Alice and Bob obtain the same 0 / 1 random number paraChoice, which is used for random filtering to obtain data for parameter estimation and data negotiation. S2, parameter estimation data preparation: x and y are selected using paraChoice to be used for parameter estimation xpara, ypara and data negotiation xmulti, ymulti; at the same time, Bob calculates the variance vary of ypara. S3, data transmission: Bob sends the calculated ypara and variance versus to Alice for parameter estimation. S4, parameter estimation calculation: Alice uses the obtained xpara, ypara, and vary to perform parameter estimation calculation, thus obtaining the parameter estimation result; In step S1, Alice and Bob obtain consistent 0 / 1 random numbers paraChoice and Alice obtain the original Gaussian random number x from the post-processing basis comparison process, or Bob obtains the original Gaussian random number y from the post-processing basis selection process, and so on. The data negotiation includes reverse data negotiation.

2. The parameter estimation process optimization method for CV-QKD systems according to claim 1, characterized in that, In step S1, the random number paraChoice is obtained through a random number generator.

3. The parameter estimation process optimization method for CV-QKD systems according to claim 1, characterized in that, In step S1, the random number paraChoice is obtained from Bob's end and sent to Alice's end.

4. The parameter estimation process optimization method for CV-QKD systems according to claim 1, characterized in that, In step S1, the random number paraChoice is read from random numbers pre-stored on Alice's end and Bob's end.

5. The parameter estimation process optimization method for CV-QKD systems according to claim 4, characterized in that, The random numbers pre-stored on the Alice and Bob ends are updated periodically.

6. A parameter estimation process optimization device suitable for CV-QKD systems, characterized in that, include: The module includes a random number generation module, a parameter estimation data preparation module, and a parameter estimation data calculation module. The random number generation module is used to generate 0 / 1 random numbers paraChoice; The parameter estimation data preparation module includes parameter estimation data preparation module A and parameter estimation data preparation module B. Parameter estimation data preparation module A is used by Alice to obtain a Gaussian random number x from the basis comparison module, obtain a 0 / 1 random number paraChoice from the random number generation module, filter x using paraChoice to obtain data for parameter estimation xpara and data negotiation xmulti, and send xpara to the parameter estimation data calculation module. Parameter estimation data preparation module B is used by Bob to obtain a Gaussian random number y from the basis selection module, obtain a 0 / 1 random number paraChoice consistent with that in parameter estimation data preparation module A from the random number generation module, filter y using paraChoice to obtain data for parameter estimation ypara and data negotiation ymulti, and calculate the variance vary using the filtered ypara. The parameter estimation data calculation module is used to complete the parameter estimation calculation process and calculate the security code rate. Among them, Alice and Bob obtain consistent 0 / 1 random numbers paraChoice and Alice obtain the original Gaussian random number x from the post-processing basis comparison process, or Bob obtains the original Gaussian random number y from the post-processing basis selection process, which are performed simultaneously. The data negotiation includes reverse data negotiation.

7. The parameter estimation process optimization device for CV-QKD systems according to claim 6, characterized in that, The random number paraChoice is obtained through a random number generator.

8. The parameter estimation process optimization device for CV-QKD systems according to claim 6, characterized in that, The random number paraChoice is obtained from Bob's end and sent to Alice's end, or it is read from a consistent random number pre-stored at both ends. The pre-stored consistent random number is updated periodically.

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