Quantum key distribution parameter optimization method in space-frequency multiplexing common-fiber transmission system

Abstract

The invention discloses a quantum key distribution parameter optimization method in a space-frequency multiplexing common-fiber transmission system. The method is suitable for a quantum key distribution light-emitting network of space-frequency multiplexing common-fiber transmission. The invention provides a quantum key distribution parameter optimization method based on a coordinate descent method, which mainly considers the problem that the quantum key distribution performance of fixed parameter setting is reduced due to channel parameter changes such as background noise and channel attenuation of a quantum channel in a space-frequency multiplexing common-fiber transmission system. The method aims at optimizing quantum key distribution parameters in real time according to channel parameters and improving quantum channel performance. Technical support is provided for fusion transmission of quantum key distribution and a classic optical network, and the application process of quantum key distribution is promoted.

Description

Technical Field

[0001] This patent relates to the field of quantum communication, and more particularly to quantum key distribution technology for integrated transmission with classical optical networks. This method can optimize the parameters of quantum key distribution in a space-frequency multiplexed co-fiber transmission system in real time based on the noise generated by the classical signal on the quantum channel and the channel attenuation changes, thereby improving the performance of quantum key distribution. Background Technology

[0002] Quantum Key Distribution (QKD) technology, based on the fundamental principles of quantum mechanics and combined with the "one-time pad" technique, can provide theoretical security guarantees for optical networks. With the development of QKD, significant progress has been made in extending secure transmission distances and improving secure key rates.

[0003] To advance the application of QKD (Quantum-Know-Device) technology, integrating quantum signals and classical optical networks using wavelength division multiplexing (WDM) and space division multiplexing (SDM) to achieve co-fiber transmission of both signals is a significant development trend. While co-fiber transmission can reduce fiber deployment costs, it introduces new challenges. Weak quantum signals are susceptible to noise interference from classical signals, such as Raman scattering noise and four-wave mixing noise. This noise is in-band noise in QKD systems and therefore cannot be filtered out. Furthermore, in co-fiber transmission systems, the transmission power of the classical signal, the channel positions of the quantum signal and the classical channel, and the quantum signal transmission path all undergo significant changes, leading to substantial alterations in channel parameters such as background noise power and channel attenuation of the quantum channel. Existing co-fiber transmission schemes typically employ fixed-parameter QKD schemes, where the transmission intensity and probability of the QKD signal state and decoy state are set to fixed values. Fixed-parameter QKD schemes experience a significant performance degradation when channel parameters change, failing to achieve optimal QKD system operation.

[0004] In summary, current co-fiber transmission systems based on space-frequency multiplexing technology urgently need an optimization algorithm that can optimize QKD parameters in real time based on changes in channel parameters. Summary of the Invention

[0005] This patent addresses the application scenario of co-fiber transmission of classical and quantum signals in space-frequency multiplexing systems. It designs a quantum key distribution (QKD) parameter optimization algorithm for such systems, which can optimize QKD system parameters in real time, thereby improving the performance of the quantum key distribution system. The main technical highlights of this method include: proposing a quantum key distribution parameter optimization algorithm based on the coordinate descent method.

[0006] The specific technical points mentioned above are explained as follows:

[0007] A quantum key distribution (QKD) parameter optimization algorithm based on coordinate descent is proposed. Considering the noise generated by classical signals and the channel attenuation variations in the quantum channel within a space-frequency multiplexed co-fiber transmission system, the algorithm sequentially searches the feasible intervals of transmission intensity and transmission probability using the coordinate descent method to find the optimal parameter set, thereby improving QKD performance. The algorithm is implemented in the following steps:

[0008] S1. Calculate the noise power generated by the classical signal and the attenuation of the quantum channel in the space-frequency multiplexing co-fiber transmission system.

[0009] S2. Set the initial parameter values ​​for the optimization method, including the initial signal state strength μ. 1 The initial value of the deceptive state strength v 1 and initial value of signal state emission probability Set the range of signal strength values ​​[μ] L ,μ U Set the range of values ​​for the decoy state strength [v] L ,v U Set the range of values ​​for the signal state transmission probability. Set the maximum number of iterations N. iter The required optimization accuracy Acc and the number of interval divisions N part ;

[0010] S3. For the i-th iteration, fix the decoy state strength and the signal state emission probability, and divide the signal state strength into N equal intervals within its value range to obtain N. part Each intensity value updates the signal state intensity. R(·) calculates the security key rate based on the input parameters; it updates the range of signal state strength values, with the lower bound of the signal state strength updated to μ. L =max(μ i+1 -(μ U -μ L ) / N part ,μ L The upper bound of the signal state strength is updated to μ. U =min(μ i+1 +(μ U -μ L ) / N part ,μ U );

[0011] S4. Given a fixed signal state strength and signal state emission probability, the decoy state strength is divided into N values ​​at equal intervals within its range. part The strength value will be updated to the decoy state strength. Update the range of values ​​for the decoy state strength: Update the lower bound of the decoy state strength to v. L=max(v i+1 -(v U -v L ) / N part ,v L The upper bound of the signal state strength is updated to v. U =min(v i+1 +(v U -v L ) / N part ,v U );

[0012] S5. With fixed signal state strength and decoy state strength, the channel state transmission probability is divided at equal intervals within its value range to obtain N. part Each intensity value updates the signal state emission probability to... Update the range of values ​​for the signal state emission probability: Update the lower bound of the signal state emission probability to... The upper bound probability of signal state transmission is updated to

[0013] S6. Check if the maximum number of iterations N has been reached. iter If the maximum key generation rate change between the current iteration and the previous iteration meets the required optimization accuracy Acc, then the iteration ends and the signal state strength, decoy state strength, and signal state emission probability are output. If the requirements are not met, the iteration count is increased by 1 and the process returns to step S3 to continue optimization.

[0014] The method described in this patent can be applied to space-frequency multiplexing co-fiber transmission systems to optimize QKD parameters in real time, thereby improving the QKD security key rate and transmission distance. The implementation of this method can lay the foundation for the integration of QKD with classic optical networks and positively promote the improvement of QKD performance in co-fiber transmission systems. Attached Figure Description

[0015] Appendix Figure 1 This is a flowchart illustrating the execution of a quantum key distribution parameter optimization method based on coordinate descent. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. The method workflow is attached. Figure 1 As shown:

[0017] 1. In a wavelength division-space division multiplexing system, first calculate the noise generated by the classical signal and the channel attenuation of the quantum channel under the channel allocation condition.

[0018] 2. Set the initial value of the signal state strength to μ. 1 =0.75 photons per pulse, with the initial value of the decoy state intensity set to v. 1 =0.2 photons per pulse, with the emission probability of the signal state set to... Set the range of signal strength values ​​[μ] L ,μ U ] = [0,1], setting the range of values ​​for the decoy state strength [v L ,v U [] = [0,1], sets the range of values ​​for the signal state transmission probability. Set the maximum number of iterations N for the algorithm. iter =10, the required optimization precision Acc = 1e-6 and the number of interval partitions N part =100.

[0019] 3. With fixed decoy state strength and signal state emission probability, from [μ L ,μ U Medium interval extraction of N part There are points, in N. part Find the value with the highest key generation rate among the signal state strengths, and update the signal state strength accordingly, while also updating the upper and lower bounds of the range of signal state strength values.

[0020] 4. With fixed signal state strength and signal state emission probability, from [v L v U Medium interval extraction of N part There are points, in N. part Find the decoy state strength with the highest key generation rate and update the decoy state strength accordingly, while also updating the upper and lower bounds of the decoy state strength value range.

[0021] 5. Fix the signal state strength and the decoy state strength, from Medium interval extraction of N part There are points, in N. part Find the value with the highest key generation rate among the signal state transmission probabilities, and update the signal state transmission probability accordingly, while also updating the upper and lower bounds of the range of signal state transmission probability values.

[0022] 6. Check if the number of iterations exceeds the set N. iter If the improvement in key generation rate is lower than the set Acc, the algorithm terminates and outputs the final signal state strength, decoy state strength, and signal state emission probability. If the requirements are not met, the iteration count is incremented by 1, and the process returns to step 3 and continues.

Claims

1. A quantum key distribution parameter optimization method in a space-frequency multiplexing co-fiber transmission system, aiming to improve the performance of the quantum channel in the co-fiber transmission system through quantum key distribution parameter optimization, characterized in that A. A quantum key distribution parameter optimization method based on the coordinate descent method is proposed. According to the changes of the noise generated by the classical signal and the channel attenuation of the quantum channel in the space-frequency multiplexing co-fiber transmission system, the feasible interval of the transmission intensity and the transmission probability is searched in turn according to the idea of coordinate descent to find the optimal parameter group.

2. The method of claim 1, proposes a quantum key distribution parameter optimization method based on coordinate descent method, characterized in that, Including the following steps: S1, calculating the noise power generated by the classical signal and the attenuation of the quantum channel in the space-frequency multiplexing co-fiber transmission system; S2, set initial parameter value of optimization method, initial value of signal state intensity μ 1 , initial value of decoy state intensity v 1 , and initial value of signal state emission probability Set the value range of signal state intensity [μ L , μ U ], set the value range of decoy state intensity [v L , v U ], set the value range of signal state emission probability Set the maximum number of iterations N iter , the required optimization accuracy Acc and the number of interval divisions N part ; S3, for the i-th iteration, fixing the intensity of the decoy state and the emission probability of the signal state, equally dividing the intensity of the signal state in its value range to obtain N part intensity values, updating the intensity of the signal state to R(·) realizes the function of calculating the security key rate according to the input parameters; the value range of the intensity of the signal state is updated, and the lower bound of the intensity of the signal state is updated to μ L = max(μ i+1 -(μ U -μ L ) / N part ,μ L ); the upper bound of the intensity of the signal state is updated to μ U = min(μ i+1 +(μ U -μ L ) / N part ,μ U ); S4, the fixed signal state intensity and signal state emission probability, the decoy state intensity is equally divided in its value range to obtain N part intensity values, the decoy state intensity is updated as The value range of the updated decoy state intensity: the lower limit of the decoy state intensity is updated as v L = max(v i+1 -(v U -v L ) / N part , v L ), and the upper limit of the signal state intensity is updated as v U = min(v i+1 +(v U -v L ) / N part , v U ). S5, the fixed signal state intensity and the intensity of the decoy state, the channel state emission probability is equally divided in its value range to obtain N part The signal state emission probability is updated as The value range of the updated signal state emission probability is: the lower bound of the signal state emission probability is updated as The upper bound of the signal state emission probability is updated as S6, detecting whether the maximum iteration number N is reached iter or the maximum key generation rate change between this iteration and the last iteration meets the required optimization accuracy Acc, if the above requirement is met, the iteration is ended, and the signal state intensity, the decoy state intensity, and the signal state emission probability at this time are output; if the requirement is not met, the iteration number is increased by 1, and the optimization is continued by returning to step S3.

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