A method and system for estimating parameters of a continuous-variable quantum key distribution system

Abstract

The application relates to the technical field of quantum communication transmission, and discloses a continuous variable quantum key distribution system parameter estimation method and system, wherein the method comprises the following steps: acquiring original data in an original data buffer pool; outputting sampling data by adopting an index sampling method; outputting multiplexing sampling data that passes authentication by adopting a message authentication code checking method; outputting grouping phase estimation values by adopting a grouping phase searching method; outputting phase compensation data by adopting a segmented linear interpolation compensation method; and performing channel parameter estimation and security code rate determination. Since the application discloses the public information of the multiplexing phase compensation process and the parameter estimation process, and performs message authentication on network interaction data, the original data utilization rate and the code rate are improved while ensuring the parameter calculation accuracy, and the accuracy and safety of the continuous variable quantum key distribution system are ensured.

Description

Technical Field

[0001] This invention relates to the field of quantum communication transmission technology, and particularly to a method and system for estimating parameters of a continuous-variable quantum key distribution system. Background Technology

[0002] Continuous-variable quantum key distribution (CV-QKD) is a technique for secure key distribution based on continuous-variable quantum states (such as coherent states). In practical systems, due to phase noise, losses, and potential eavesdropping during channel transmission, the quantum state received by the receiver will be distorted. Therefore, accurate estimation of channel parameters (such as transmittance and over-noise) is necessary to calculate the secure key rate and determine whether the current channel conditions are secure. The accuracy of parameter estimation directly determines the security and generation efficiency of the final key.

[0003] Currently, CV-QKD systems typically require the disclosure or occupancy of a portion of the original data during the phase compensation stage, and another portion during the parameter estimation stage. While this approach can separately complete phase drift correction and channel parameter calculation, it reduces the proportion of data available for key negotiation under actual high-speed transmission and limited code length conditions, decreasing the utilization rate of the original data and consequently affecting the final code generation rate. Especially under conditions of link phase drift, excessive noise fluctuations, and numerous interaction rounds, if the phase compensation and parameter estimation processes consume data independently, it not only increases the amount of publicly disclosed interactive data but also expands the impact of data tampering, replay, or substitution attacks on the parameter estimation results, making it difficult for the system to simultaneously guarantee computational accuracy, original data utilization, and the reliability of interactive data.

[0004] Therefore, a secure phase compensation and parameter estimation method is urgently needed to reuse the public information of the phase compensation and parameter estimation processes in continuous variable quantum key distribution systems. This would allow the same authenticated sampled data to be used for both phase compensation and channel parameter estimation, significantly improving the utilization rate of the original data while ensuring computational accuracy and effectively increasing the system's key generation rate. Simultaneously, message authentication is required for network interaction data such as sampling indices, round identifiers, orthogonal component data, and publicly available phase compensation parameters to confirm the authenticity and integrity of the network interaction data, thereby ensuring the accuracy and security of the continuous variable quantum key distribution system. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a parameter estimation method for continuous-variable quantum key distribution systems. This method aims to solve the technical problem that existing technologies require a large amount of raw key data for phase compensation and parameter estimation, especially under conditions of link phase drift and excessive noise fluctuations, making it impossible to balance the accuracy of parameter estimation with the utilization rate of raw data.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for estimating parameters of a continuous variable quantum key distribution system.

[0007] The method for estimating parameters of a continuous-variable quantum key distribution system includes: Step S10: Obtain the original data buffer pool in the continuous variable quantum key distribution system, and perform data extraction task based on the original data buffer pool using the index sampling method, and output the sampled data; Step S20: Based on the sampled data, perform a data consistency verification task using the message authentication code verification method, and output the reusable sampled data that has passed the verification. Step S30: Based on the multiplexed sampling data that has passed the identification, the receiving end orthogonal component phase estimation task is performed using the group phase search method, and the group phase estimation value is output; Step S40: Based on the grouped phase estimation value, a piecewise linear interpolation compensation method is used to perform point-by-point phase compensation for the retained data at the receiving end, and output the phase compensation data; Step S50: Based on the phase compensation data, perform channel parameter estimation and secure code rate determination, and output the retention result of the original data for this round.

[0008] Preferably, step S10, which involves obtaining the original data buffer pool in the continuous-variable quantum key distribution system, performing a data extraction task based on the original data buffer pool using an index sampling method, and outputting the sampled data, specifically includes: Step S101: Obtain the original data buffer pool in the continuous variable quantum key distribution system. When the amount of effective data in the original data buffer pool reaches the preset processing length... At that time, the true random number sequence and data volume generated by the quantum random number source are read. ; Step S102: Map the true random number sequence to a sampling index set, wherein the index values ​​in the sampling index set are unique. Step S103: Extract the orthogonal component data of the transmitting end and the orthogonal component data of the receiving end from the original data buffer pool according to the sampling index set to obtain the sampling data; Among them, the preset processing length This refers to the amount of effective raw data obtained in this round of exploration. Used to represent the amount of data used for phase estimation and parameter estimation, and Not less than the preset minimum sampling ratio.

[0009] Preferably, step S20, which involves performing a data consistency verification task based on the sampled data using a message authentication code verification method and outputting the verified reused sampled data, specifically includes: Step S201: Using a preset authentication key, the sending end calculates the message authentication code based on the index information, orthogonal component data, and round identifier corresponding to the sampled data to obtain the sending end authentication value. ; Step S202: Using the same authentication key as the sender, the receiver calculates the message authentication code on the received index information, orthogonal component data, and round identifier to obtain the receiver authentication value. ; Step S203: Set the sending end authentication value Distinguishing value from the receiving end The data is compared. If the two data points match, the reused sampling data that has passed the identification is output. If the two data points do not match, the sampling data of this round is discarded and the identification failure flag is recorded. Finally, the reused sampling data that has passed the identification is output. The message authentication code calculation employs either a block cipher message authentication code algorithm or a hash message authentication code algorithm.

[0010] Preferably, step S30, which involves performing the receiver quadrature component phase estimation task using a grouped phase search method based on the identified multiplexed sampling data and outputting the grouped phase estimation value, specifically includes: Step S301: Divide the verified reused sampling data into groups according to time sequence. Each packet contains two quadrature component data sets: one receiving quadrature component data set and one transmitting quadrature component data set. The transmitting quadrature component data set includes data from the transmitting end... Orthogonal component data, transmitter Orthogonal component data; receiver orthogonal component data includes receiver data. Orthogonal component data and receiver Orthogonal component data; Step S302: For the first Grouped according to candidate phase values The orthogonal component data at the receiving end is rotated to obtain the rotated orthogonal component data at the receiving end. Step S303: Calculate the sum of errors between the rotated quadrature component data at the receiving end and the quadrature component data at the transmitting end, and determine the candidate phase value that maximizes the sum of errors as the phase estimate of the k-th packet. ; Wherein, the rotational transformation satisfies:

[0011] in, Indicates the first The first group The receiving end corresponding to each data point Orthogonal component data; Indicates the first The first group The receiving end corresponding to each data point Orthogonal component data; and Representing candidate phase values The corresponding cosine and sine values; Indicates the first The first group Data points are processed by candidate phase values The receiver after rotation transformation Orthogonal component data; Indicates the first The first group Data points are processed by candidate phase values The receiver after rotation transformation Orthogonal component data; The error and value satisfy:

[0012] in, Indicates the first Each group at the candidate phase value The following errors and values; Indicates the first The set of data point indices involved in phase estimation within each group; Indicates the first The first group The sending end corresponding to each data point Orthogonal component data, Indicates the first The sending end in each group The average value of orthogonal component data; Indicates the first The first group The sending end corresponding to each data point Orthogonal component data, Indicates the first The sending end in each group The average value of orthogonal component data; Indicates the first The first group Data points are processed by candidate phase values The receiver after rotation transformation Orthogonal component data, Indicates the first Candidate phase values ​​in each group The receiver after rotation transformation The average value of orthogonal component data; Indicates the first The first group Data points are processed by candidate phase values The receiver after rotation transformation Orthogonal component data, Indicates the first Candidate phase values ​​in each group The receiver after rotation transformation The average value of the orthogonal component data.

[0013] Preferably, step S40, which involves performing point-by-point phase compensation of the retained data at the receiving end using a piecewise linear interpolation compensation method based on the grouped phase estimation values, and outputting the phase compensation data, specifically includes: Step S401: Obtain the first group phase estimate based on the group phase estimate. Group 1, No. Group 1 and the 2nd group The phase estimates corresponding to each group , and ; Step S402: Obtain the data points to be compensated, and based on the data points to be compensated in the first... The first group For each data point, piecewise linear interpolation is used to calculate the compensation phase corresponding to that data point. ; Step S403: According to the compensation phase The data retained at the receiving end is rotated point by point to obtain phase-compensated data; Wherein, the compensation phase satisfy:

[0014] The point-to-point rotation compensation satisfies:

[0015] in, This indicates the number of data points contained in a single group. Indicates the first The first group Compensation phase corresponding to each data point The cosine value; Indicates the first The first group Compensation phase corresponding to each data point The sine value.

[0016] Preferably, step S50, the step of estimating channel parameters based on the phase compensation data, specifically includes: Step S501: Obtain the first orthogonal component data and the second orthogonal component data of the transmitting end, and at the same time obtain the phase-compensated first orthogonal component data and the second orthogonal component data of the receiving end according to the phase compensation data; Step S502: Calculate the first channel transmittance based on the first orthogonal component data from the transmitting end and the first orthogonal component data after phase compensation from the receiving end. and first over-noise ; Step S503: Calculate the second channel transmittance based on the second orthogonal component data from the transmitting end and the second orthogonal component data after phase compensation from the receiving end. Second over-noise ; Step S504: Calculate the transmittance of the first channel. The second channel transmittance The first over-noise and the second over-noise This serves as the result of the channel parameter estimation in this round; Wherein, the first channel transmittance The first over-noise The second channel transmittance and the second over-noise satisfy:

[0017]

[0018]

[0019]

[0020] in, This represents the first channel transmittance, which is the channel transmittance corresponding to the first orthogonal component. This represents the first over-noise, which is the over-noise corresponding to the first orthogonal component. This represents the second channel transmittance, which is the channel transmittance corresponding to the second orthogonal component. This represents the second over-noise, which is the over-noise corresponding to the second orthogonal component. This represents the first orthogonal component data in the sampled data from the sending end; This represents the second orthogonal component data in the sampled data from the sending end; This represents the first orthogonal component data in the sampled data after phase compensation at the receiving end; This represents the second orthogonal component data in the sampled data after phase compensation at the receiving end; This represents the calculated covariance between the first orthogonal component data at the transmitting end and the first orthogonal component data at the receiving end. This represents the calculated covariance between the second orthogonal component data at the transmitting end and the second orthogonal component data at the receiving end. This represents the variance estimate of the first orthogonal component data at the transmitting end; This represents the calculated variance of the second orthogonal component data at the transmitting end. This represents the calculated variance of the first orthogonal component data at the receiving end. This represents the calculated variance of the second orthogonal component data at the receiving end.

[0021] Preferably, step S50, which involves estimating channel parameters and determining the secure code rate based on the phase compensation data, and outputting the retained results of the original data for this round, further includes: Step S505: Based on the first channel transmittance The first over-noise The second channel transmittance and the second over-noise Calculate the amount of information that the eavesdropper can obtain; Step S506: Obtain negotiation efficiency Mutual information between the sending and receiving ends , limited code length extra items According to the efficiency of negotiation Mutual information between the sending and receiving ends , limited code length extra items And the amount of information that the eavesdropper can obtain, to calculate the system security code rate. ; Step S507: When the system security code rate Greater than zero, the first channel transmittance Not lower than the first transmittance threshold, the second channel transmittance Not lower than the second transmittance threshold, the first over-noise The first over-noise threshold and the second over-noise threshold are not exceeded. If the data does not exceed the second noise threshold, the result of retaining the original data in this round is "retained"; otherwise, the result of retaining the original data in this round is "discarded". Among them, the system security code rate satisfy:

[0022] in, Indicates the system security code rate; This represents the amount of data used to generate the final symmetric key; This indicates the amount of effective data obtained in this round of exploration; Indicates the efficiency of the negotiation; This represents the mutual information between the sending and receiving ends; This represents the amount of information that the eavesdropper can obtain, and the amount of information is determined by the first channel transmittance. First over-noise Second channel transmittance Second over-noise Sure; The additional term represents the condition of finite code length; the first transmittance threshold, the second transmittance threshold, the first over-noise threshold, and the second over-noise threshold are determined according to the parameters of the reverse negotiation algorithm used in the continuous variable quantum key distribution system.

[0023] This invention also provides a parameter estimation system for a continuous-variable quantum key distribution system, comprising: The data sampling module is used to obtain the original data buffer pool in the continuous variable quantum key distribution system, and to perform data extraction tasks based on the original data buffer pool using the index sampling method, and output the sampled data. The message authentication module is used to perform a data consistency verification task based on the sampled data using a message authentication code verification method, and output the reusable sampled data that has passed authentication. The phase estimation module is used to perform the receiving end quadrature component phase estimation task based on the identified multiplexed sampling data using a grouped phase search method, and output the grouped phase estimation value. The phase compensation module is used to perform point-by-point phase compensation of the retained data at the receiving end based on the grouped phase estimation value using a piecewise linear interpolation compensation method, and output phase compensation data. The parameter estimation module is used to perform channel parameter estimation based on the phase compensation data and output the channel parameter estimation results for this round. The security rate determination module is used to determine the security rate based on the channel parameter estimation results of this round and output the retention result of the original data of this round.

[0024] The present invention also provides a parameter estimation device for a continuous variable quantum key distribution system, the continuous variable quantum key distribution system parameter estimation device comprising: a memory, a processor, and a continuous variable quantum key distribution system parameter estimation program stored in the memory and executable on the processor, wherein the continuous variable quantum key distribution system parameter estimation program implements the above method when executed by the processor.

[0025] The present invention also provides a computer program product, the computer program product including a continuous variable quantum key distribution system parameter estimation program, which implements the above method when executed by a processor.

[0026] The beneficial effects of this invention are as follows: 1. This invention achieves efficient and robust estimation of channel parameters for CV-QKD systems by constructing a complete technical chain of "index sampling - message authentication - block phase search and interpolation compensation - parameter estimation and security determination". Index sampling ensures the randomness and representativeness of the data samples used for parameter estimation; message authentication code verification guarantees the consistency of the data used for estimation from the source, preventing parameter misestimation due to data tampering; the combination of block phase search and piecewise linear interpolation compensation can quickly and accurately estimate and compensate for time-varying phase drift at the receiver.

[0027] 2. This invention directly links the security code rate determination with the channel parameter estimation results and sets multi-dimensional determination conditions (transmissivity threshold, over-noise threshold, security code rate greater than zero), enabling the system to intelligently and rigorously determine whether the current channel conditions are suitable for retaining the data of this round for subsequent key generation based on the real-time estimated parameters. This improves the efficiency of parameter estimation while ensuring the information theory security of the final generated key. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the first embodiment of a continuous variable quantum key distribution system parameter estimation method according to the present invention. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0030] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0031] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the parameter estimation method for a continuous variable quantum key distribution system according to the present invention. The first embodiment of the parameter estimation method for a continuous variable quantum key distribution system according to the present invention is presented.

[0032] In the first embodiment, the method for estimating parameters of a continuous-variable quantum key distribution system includes: Step S10: Obtain the original data buffer pool in the continuous variable quantum key distribution system, and perform data extraction task based on the original data buffer pool using the index sampling method, and output the sampled data; In this step, the "raw data buffer" refers to the temporary storage area in the CV-QKD system for the unprocessed raw orthogonal component data (such as x and p components) obtained by modulation at the transmitter and detection at the receiver. The "index sampling method" refers to using a quantum random number source to generate a set of unique sampling indices from a truly random number sequence, and then extracting data from the buffer at the corresponding positions based on this index set. The "sampled data" output by this method includes a subset of the orthogonal component data corresponding to the transmitter and receiver. This subset will serve as the basic input data for subsequent phase and parameter estimation, and its randomness ensures the unbiasedness of the estimation.

[0033] By introducing index sampling based on true random numbers, statistically representative data samples are provided for the entire parameter estimation process. This ensures that the data used for estimation is randomly and unbiasedly selected from a large amount of raw data, avoiding estimation biases that may be introduced by data order or specific patterns, and providing a reliable data foundation for obtaining accurate channel parameter estimation results. Simultaneously, by preset processing length and minimum sampling ratio, this step automates processing triggering and data volume control.

[0034] Compared to traditional techniques that may employ fixed-interval sampling or simple sequential data extraction, the index sampling method employed in this invention more effectively resists potential attacks targeting the sampling pattern, enhancing the anti-interference capability of the parameter estimation process. Simultaneously, the use of a quantum random number source ensures the unpredictability of the sampling index, further strengthening the security of the sampling process and making it difficult for eavesdroppers to systematically influence the parameter estimation results by predicting the sampling pattern.

[0035] For example, suppose the system has accumulated N = 1,000,000 valid data points in this round of detection and stored them in a buffer pool. Step S10 generates s = 10,000 unique random indices by reading a quantum random number source (satisfying s / N = 1% is not less than a preset minimum proportion). Based on these 10,000 indices, the system precisely extracts 10,000 pairs of data from the transmitting end and 10,000 pairs of data from the receiving end from the corresponding positions in the buffer pool. These 10,000 pairs of sampled data serve as the starting point for all subsequent processing. Their randomness ensures that even if the original data has some temporal correlation, the estimation result can still reflect the overall statistical characteristics of the channel.

[0036] Step S20: Based on the sampled data, perform a data consistency verification task using the message authentication code verification method, and output the reusable sampled data that has passed the verification. The "Message Authentication Code Verification Method" in this step is a cryptographic primitive used to verify the integrity and authenticity of data during transmission. Specifically, the sender and receiver use a shared, pre-set "authentication key" to calculate specific data content (including the index information of the sampled data, the orthogonal component data itself, and the round identifier of the processing round) to generate a "Sender Authentication Value (MAC)". A "and "Receiver Authentication Value (MAC)" B The core of the verification lies in comparing whether the two identification values ​​are consistent. The "multiplexed sampled data that has passed the verification" output in this step refers to the sampled data that has passed the above consistency verification and has been confirmed as not to have been tampered with during transmission. This data will be trusted and used for subsequent critical phase estimation.

[0037] This step embeds a lightweight but crucial security check within the parameter estimation process. It ensures that the sampled data set used for parameter estimation, transmitted from the sender to the receiver, is complete and consistent, preventing eavesdroppers from tampering with, replaying, or replacing the sampled data during data transmission.

[0038] Compared to existing technologies that may neglect data security during the parameter estimation stage, or that employ complex and costly complete data encryption transmission schemes, this invention introduces message authentication code verification. With minimal computational and communication overhead (only transmitting and comparing a fixed-length authentication value), it achieves efficient authentication of critical estimation data, preventing eavesdroppers from misleading the system into making incorrect security decisions by tampering with the estimation data.

[0039] For example, after obtaining the sampled data in step S10, the sending end uses a preset key K to calculate the current round identifier (e.g., sequence number 001), the sampling index set {index1, index2, ...}, and the corresponding authentication value (MAC) of the sending end's orthogonal component data using the HMAC-SHA256 algorithm. A This information is then sent to the receiving end. Upon receiving the index, data, and round identifier, the receiving end uses the same key K and algorithm to calculate the receiver authentication value (MAC). B If the two are completely consistent, it means that the data has not been tampered with during transmission, and the receiving end retains the data for the next step; if they are inconsistent, all sampling data in this round is immediately discarded, and an alarm is recorded, thereby avoiding all subsequent calculation errors and security misjudgments caused by using untrusted data.

[0040] Step S30: Based on the multiplexed sampling data that has passed the identification, the receiving end orthogonal component phase estimation task is performed using the group phase search method, and the group phase estimation value is output; The "group phase search method" in this step estimates the overall phase rotation at the receiver caused by channel phase noise for the validated sampled data. Specifically, the temporally continuous sampled data is first divided into m sequential groups. For each group, it is assumed that its internal data is affected by a uniform phase rotation θ. By traversing a preset set of candidate phase values, rotation transformation is performed on all orthogonal components of the receiver data within the group, and the sum of squared errors between the transformed data and the corresponding transmitter data is calculated. The candidate phase value that minimizes this error sum is determined as the "group phase estimate" for that group. Essentially, this method finds an optimal phase rotation angle within each group, ensuring that the rotated receiver data is as close as possible to the transmitter data overall.

[0041] This step provides an efficient and practical phase drift estimation scheme. By grouping the data, it transforms the continuous time-varying phase estimation problem into the estimation problem of a constant phase over a series of discrete time intervals, reducing computational complexity. Using an error minimization criterion for the search, it can robustly estimate the dominant phase rotation angle from noisy data.

[0042] Compared to traditional phase estimation methods that may require complex phase-locked loops or overall fitting based on large amounts of data, the grouped phase search method of this invention is more suitable for the characteristics of data block processing in CV-QKD. It does not require continuous pilot signals, but directly uses the data itself for estimation, improving spectral efficiency. Grouped processing enables the algorithm to track phase change trends, and the search process can be parallelized, which is beneficial for real-time processing in high-speed systems, improving the timeliness of phase estimation.

[0043] For example, suppose there are 10,000 pairs of successfully authenticated multiplexed sampled data, which are divided into m=100 groups in chronological order, with L=100 data points in each group. For the k-th group, the system presets candidate phase values ​​to be discretized from 0 to 2π with a certain step size. For each candidate θ, all 100 receiver data points in the group are rotated according to the formula, and then the sum of squared Euclidean distances between these 100 rotated points and the corresponding transmitter endpoints is calculated. After traversing all candidate θs, the θ that minimizes f(θ) is found. min This value represents the approximate rotation (θ) of the received signal relative to the transmitted signal within the time window of these 100 data points. min Radians. Perform this operation on all 100 groups to obtain a sequence of 100 phase estimates.

[0044] Step S40: Based on the grouped phase estimation value, a piecewise linear interpolation compensation method is used to perform point-by-point phase compensation for the retained data at the receiving end, and output the phase compensation data; The "piecewise linear interpolation compensation method" in this step aims to use the discrete group phase estimates obtained in step S30 to perform fine, pointwise phase compensation on all the original data (not just sampled data) that needs to be retained at the receiver. Its core idea is to model the phase change within each group as linear. For the i-th data point within a group, its compensation phase is calculated using the phase estimates of the preceding and following groups through a piecewise linear interpolation formula. Then, using this calculated compensation phase for that specific data point, a rotation transformation is performed on the orthogonal component data at that point at the receiver, thus obtaining "phase-compensated data." This process eliminates the phase rotation introduced by the channel, making the receiver data as phase-aligned as possible with the transmitter.

[0045] This step transitions from coarse phase estimation at the "group level" to precise phase compensation at the "point-by-point level." The piecewise linear interpolation model is simple and effective, smoothly fitting the phase variation trend between groups and estimating subtle phase changes within groups. Through point-by-point compensation, it corrects the distortion caused by phase drift at each data point to the greatest extent, providing high-quality phase-aligned data for subsequent calculations of channel parameters (such as transmittance and over-noise).

[0046] Compared to the simple method of uniformly compensating all data within a group using only the group center phase value, the piecewise linear interpolation compensation of this invention takes into account the continuous change of phase over time, resulting in more precise compensation, especially suitable for cases where the phase drift rate is not constant. This reduces the possibility of "overcompensation" or "undercompensation" of data points, thereby reducing residual phase compensation errors and ultimately improving the accuracy and reliability of channel parameter estimation.

[0047] For example, for the i=30th data point in the kth group (out of 100 points), the phase estimates for the (k-1), k, and k+1th groups are known to be 0.10 rad, 0.15 rad, and 0.18 rad, respectively. Since i=30 is located in the first half of this group (1≤i≤50), according to the interpolation formula, its compensated phase = [(100-2...]. 30) / (2 100)] 0.10+[(100+2 30) / (2 100)] 0.15 = 0.14 rad. Then, the system uses a rotation matrix to rotate the original receiver data at that point in the opposite direction by a rotation angle of 0.14 rad, obtaining the phase-compensated data. A similar operation is performed on all original data points, and the final output is a complete dataset with the phase essentially corrected.

[0048] Step S50: Based on the phase compensation data, perform channel parameter estimation and secure code rate determination, and output the retention result of the original data for this round.

[0049] In step S30 and step S40, the multiplexed sampling data that has passed the identification is used to perform the quadrature component phase estimation task and the point-by-point phase compensation task at the receiving end, and in step S50, it continues to serve as the data source for channel parameter estimation, so that the phase compensation process and the parameter estimation process reuse the information corresponding to the same sampling index set.

[0050] This step comprises two core tasks: "channel parameter estimation" and "secure code rate determination." Channel parameter estimation, based on phase-compensated sampled data, calculates key parameters characterizing the channel, including the "channel transmittance T" and "over-noise ε" corresponding to each of the two orthogonal components (e.g., x and p). These parameters are obtained by calculating statistics such as covariance and variance between the transmitting and receiving data, reflecting the channel's attenuation and noise level. Secure code rate determination, on the other hand, comprehensively utilizes the estimated channel parameters, negotiation efficiency, mutual information, and finite code length correction terms to calculate the theoretically extractable secure key rate R under the current channel conditions. The final "retention result" is a binary decision: based on whether the secure code rate is greater than zero and whether each channel parameter is within a preset security threshold range, it determines whether to retain all original data from this round for subsequent post-processing (such as data negotiation and security enhancement) to generate the final key, or to discard it all.

[0051] This step is the final goal and decision point of the entire parameter estimation process, directly linking the quantitative evaluation of the channel (parameter estimation) with the system's core security objective (key generation). By accurately calculating the security code rate and combining it with multiple threshold judgments, only data blocks that are determined to be "secure" and "valid" at the parameter estimation level will proceed to the subsequent key extraction process.

[0052] Compared to traditional methods that may rely on a single parameter (such as bit error rate) or a simple threshold for rough judgment, this invention employs a secure bit rate formula based on information theory for comprehensive judgment, with its security rigorously guaranteed by theory. Simultaneously, the introduction of a finite code length correction term makes the estimation closer to the actual system performance. Including independent thresholds for transmittance and excessive noise as additional conditions provides a deeper layer of security redundancy, effectively filtering out data that, although the bit rate calculation is positive, has high excessive noise, preventing subsequent data negotiation units from completing error correction.

[0053] Example 2: Furthermore, the present invention provides a parameter estimation system for a continuous variable quantum key distribution system, employing a parameter estimation method for a continuous variable quantum key distribution system as described in the above embodiments, which can solve the technical problem of parameter estimation for a continuous variable quantum key distribution system. The beneficial effects of the parameter estimation system for a continuous variable quantum key distribution system provided by the present invention are the same as those of the parameter estimation method for a continuous variable quantum key distribution system provided in the above embodiments, and other technical features of the parameter estimation system for a continuous variable quantum key distribution system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0054] Example 3: This invention provides a parameter estimation device for a continuous variable quantum key distribution system. The device includes at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the processor, which are then executed to enable the processor to perform the parameter estimation method for a continuous variable quantum key distribution system described in Example 1. The continuous variable quantum key distribution system parameter estimation device in this embodiment may include, but is not limited to, a quantum key distribution transmitter, a quantum key distribution receiver, a continuous variable quantum key distribution control host, a quantum communication exchange device, a photoelectric detection control device, a coherent detection data processing device, a quantum communication network node device, and a server or industrial control computing device with a parameter estimation program deployed thereon. The above parameter estimation device is merely an example and should not be construed as limiting the application scope of this invention. A continuous variable quantum key distribution system parameter estimation device may include a processing device (e.g., a central processing unit, a graphics processing unit, a field-programmable gate array device, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory or a program loaded from a storage device into a random access memory. The random access memory also stores various programs and data required for the operation of a continuous-variable quantum key distribution system parameter estimation device. The processing unit, read-only memory, and random access memory are interconnected via a bus. An I / O interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, hard disks; and communication devices. The communication device allows the continuous-variable quantum key distribution system parameter estimation device to communicate wirelessly or wiredly with other devices to exchange data. While a continuous-variable quantum key distribution system parameter estimation device with various systems has been described, it should be understood that it is not required to implement or possess all the systems described. Alternatively, more or fewer systems can be implemented.

[0055] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the continuous variable quantum key distribution system parameter estimation method described above. The computer program product provided by this invention can solve the technical problem of continuous variable quantum key distribution system parameter estimation. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the continuous variable quantum key distribution system parameter estimation method provided in the above embodiments, and will not be repeated here.

[0056] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0057] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0058] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for estimating parameters of a continuous-variable quantum key distribution system, characterized in that, The method includes: Step S10: Obtain the original data buffer pool in the continuous-variable quantum key distribution system, and perform a data extraction task based on the original data buffer pool using an index sampling method to output sampled data; wherein, the step of obtaining the original data buffer pool in the continuous-variable quantum key distribution system, performing a data extraction task based on the original data buffer pool using an index sampling method, and outputting sampled data specifically includes: Obtain the raw data buffer pool in the continuous variable quantum key distribution system, and when the amount of effective data in the raw data buffer pool reaches a preset processing length... At that time, the true random number sequence and data volume generated by the quantum random number source are read. ; The true random number sequence is mapped to a sampling index set, wherein the index values ​​in the sampling index set are unique. According to the sampling index set, the orthogonal component data of the transmitting end and the orthogonal component data of the receiving end are extracted from the original data buffer pool to obtain the sampling data; Among them, the preset processing length This refers to the amount of effective raw data obtained in this round of exploration. Used to represent the amount of data used for phase estimation and parameter estimation, and Not less than the preset minimum sampling ratio; Step S20: Based on the sampled data, perform a data consistency verification task using the message authentication code verification method, and output the reusable sampled data that has passed the verification. Step S30: Based on the multiplexed sampling data that has passed the identification, the receiving end orthogonal component phase estimation task is performed using the group phase search method, and the group phase estimation value is output; Step S40: Based on the grouped phase estimation value, a piecewise linear interpolation compensation method is used to perform point-by-point phase compensation for the retained data at the receiving end, and output the phase compensation data; Step S50: Based on the authenticated multiplexed sampling data and the phase compensation data, perform channel parameter estimation and secure code rate determination, and output the retention result of the original data for this round; wherein, the step of performing channel parameter estimation and secure code rate determination based on the phase compensation data and outputting the retention result of the original data for this round further includes: Obtain the channel parameter estimation results for this round, and based on the first channel transmittance in the channel parameter estimation results for this round... First over-noise Second channel transmittance Second over-noise Calculate the amount of information that the eavesdropper can obtain; Achieve negotiation efficiency Mutual information between the sending and receiving ends , limited code length extra items According to the efficiency of negotiation Mutual information between the sending and receiving ends , limited code length extra items And the amount of information that the eavesdropper can obtain, to calculate the system's security bitrate. ; When the system security code rate Greater than zero, the first channel transmittance Not lower than the first transmittance threshold, the second channel transmittance Not lower than the second transmittance threshold, the first over-noise The first over-noise threshold and the second over-noise If the data does not exceed the second noise threshold, the result of retaining the original data in this round is "retained"; otherwise, the result of retaining the original data in this round is "discarded".

2. The parameter estimation method for a continuous-variable quantum key distribution system as described in claim 1, characterized in that, Step S20, which involves performing a data consistency verification task based on the sampled data using a message authentication code verification method and outputting the verified reused sampled data, specifically includes: Step S201: Using a preset authentication key, the sending end calculates the message authentication code based on the index information, orthogonal component data, and round identifier corresponding to the sampled data to obtain the sending end authentication value. ; Step S202: Using the same authentication key as the sender, the receiver calculates the message authentication code on the received index information, orthogonal component data, and round identifier to obtain the receiver authentication value. ; Step S203: Set the sending end authentication value Distinguishing value from the receiving end The data is compared. If the two data points match, the reused sampling data that has passed the identification is output. If the two data points do not match, the sampling data of this round is discarded and the identification failure flag is recorded. Finally, the reused sampling data that has passed the identification is output. The message authentication code calculation employs either a block cipher message authentication code algorithm or a hash message authentication code algorithm.

3. The parameter estimation method for a continuous-variable quantum key distribution system as described in claim 1, characterized in that, Step S30, which involves performing quadrature component phase estimation at the receiving end based on the authenticated multiplexed sampled data using a grouped phase search method and outputting the grouped phase estimation values, specifically includes: Step S301: Divide the identified and approved reused sampling data into categories according to time sequence. Each packet contains two quadrature component data sets: one receiving quadrature component data set and one transmitting quadrature component data set. The transmitting quadrature component data set includes data from the transmitting end... Orthogonal component data, transmitter Orthogonal component data; receiver orthogonal component data includes receiver data. Orthogonal component data and receiver Orthogonal component data; Step S302: For the first Grouped according to candidate phase values The orthogonal component data at the receiving end is rotated to obtain the rotated orthogonal component data at the receiving end. Step S303: Calculate the sum of errors between the rotated quadrature component data at the receiving end and the quadrature component data at the transmitting end, and determine the candidate phase value that maximizes the sum of errors as the phase estimate of the k-th packet. .

4. The parameter estimation method for a continuous-variable quantum key distribution system as described in claim 3, characterized in that, Step S40, which involves performing point-by-point phase compensation of the retained data at the receiving end based on the grouped phase estimate using a piecewise linear interpolation compensation method, and outputting the phase compensation data, specifically includes: Step S401: Obtain the first group phase estimate based on the group phase estimate. Group 1, No. Group 1 and the 2nd group The phase estimates corresponding to each group , and ; Step S402: Obtain the data points to be compensated, and based on the data points to be compensated in the first... The first group For each data point, piecewise linear interpolation is used to calculate the compensation phase corresponding to that data point. ; Step S403: According to the compensation phase The data retained at the receiving end is rotated point by point to obtain phase-compensated data.

5. The parameter estimation method for a continuous-variable quantum key distribution system as described in claim 3, characterized in that, Step S50, the step of estimating channel parameters based on the phase compensation data, specifically includes: Step S501: Obtain the first orthogonal component data and the second orthogonal component data of the transmitting end, and at the same time obtain the phase-compensated first orthogonal component data and the second orthogonal component data of the receiving end according to the phase compensation data; Step S502: Calculate the first channel transmittance based on the first orthogonal component data from the transmitting end and the first orthogonal component data after phase compensation from the receiving end. and first over-noise ; Step S503: Calculate the second channel transmittance based on the second orthogonal component data from the transmitting end and the second orthogonal component data after phase compensation from the receiving end. Second over-noise ; Step S504: Calculate the transmittance of the first channel. The second channel transmittance The first over-noise and the second over-noise This serves as the result of the channel parameter estimation in this round.

6. A parameter estimation system for a continuous-variable quantum key distribution system, characterized in that, The system includes: The data sampling module is used to obtain the original data buffer pool in the continuous-variable quantum key distribution system, perform data extraction tasks based on the original data buffer pool using an index sampling method, and output sampled data. Specifically, the steps of obtaining the original data buffer pool in the continuous-variable quantum key distribution system, performing data extraction tasks based on the original data buffer pool using an index sampling method, and outputting sampled data include: Obtain the raw data buffer pool in the continuous variable quantum key distribution system, and when the amount of effective data in the raw data buffer pool reaches a preset processing length... At that time, the true random number sequence and data volume generated by the quantum random number source are read. ; The true random number sequence is mapped to a sampling index set, wherein the index values ​​in the sampling index set are unique. According to the sampling index set, the orthogonal component data of the transmitting end and the orthogonal component data of the receiving end are extracted from the original data buffer pool to obtain the sampling data; Among them, the preset processing length This refers to the amount of effective raw data obtained in this round of exploration. Used to represent the amount of data used for phase estimation and parameter estimation, and Not less than the preset minimum sampling ratio; The message authentication module is used to perform a data consistency verification task based on the sampled data using a message authentication code verification method, and output the reusable sampled data that has passed authentication. The phase estimation module is used to perform the receiving end quadrature component phase estimation task based on the identified multiplexed sampling data using a grouped phase search method, and output the grouped phase estimation value. The phase compensation module is used to perform point-by-point phase compensation of the retained data at the receiving end based on the grouped phase estimation value using a piecewise linear interpolation compensation method, and output phase compensation data. The parameter estimation module is used to perform channel parameter estimation based on the phase compensation data and output the channel parameter estimation results for this round. The secure code rate determination module is used to perform channel parameter estimation and secure code rate determination based on the authenticated multiplexed sampling data and the phase compensation data, and output the retention result of the original data in this round; wherein, the step of performing channel parameter estimation and secure code rate determination based on the phase compensation data and outputting the retention result of the original data in this round further includes: Obtain the channel parameter estimation results for this round, and based on the first channel transmittance in the channel parameter estimation results for this round... First over-noise Second channel transmittance Second over-noise Calculate the amount of information that the eavesdropper can obtain; Achieve negotiation efficiency Mutual information between the sending and receiving ends , limited code length extra items According to the efficiency of negotiation Mutual information between the sending and receiving ends , limited code length extra items And the amount of information that the eavesdropper can obtain, to calculate the system's security bitrate. ; When the system security code rate Greater than zero, the first channel transmittance Not lower than the first transmittance threshold, the second channel transmittance Not lower than the second transmittance threshold, the first over-noise The first over-noise threshold and the second over-noise If the data does not exceed the second noise threshold, the result of retaining the original data in this round is "retained"; otherwise, the result of retaining the original data in this round is "discarded".

7. A parameter estimation device for a continuous-variable quantum key distribution system, characterized in that, The continuous variable quantum key distribution system parameter estimation device includes: a memory, a processor, and a continuous variable quantum key distribution system parameter estimation program stored in the memory and executable on the processor. When the continuous variable quantum key distribution system parameter estimation program is executed by the processor, it implements a continuous variable quantum key distribution system parameter estimation method according to any one of claims 1 to 5.

8. A computer program product, characterized in that, The computer program product includes a parameter estimation program for a continuous variable quantum key distribution system, which, when executed by a processor, implements a parameter estimation method for a continuous variable quantum key distribution system according to any one of claims 1 to 5.

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