Performance optimization method of laser phase noise quantum random number generator

Abstract

The invention discloses a performance optimization method for a laser phase noise quantum random number generator, and the method comprises the steps: S1, evaluating the system output signal bandwidth of the laser phase noise quantum random number generator, building a relation between each component parameter of the system and the system output signal bandwidth, actual detector type selection and sampling rate setting are guided accordingly; s2, evaluating the minimum entropy of the laser phase noise quantum random number generator system, and establishing a relationship between core parameters of each component of the laser phase noise quantum random number generator system and the minimum entropy; and S3, in combination with the relationship among the component parameters of the laser phase noise quantum random number generator system, the sampling rate and the minimum entropy, obtaining experimental parameter selection for optimizing the performance of the laser phase noise quantum random number generator system so as to guide specific experimental design and implementation. According to the method, repeated tests are not needed, the implementation complexity is low, the global optimal performance can be obtained, and the method has good flexibility and practicability.

Description

Technical Field

[0001] The present application belongs to the technical field of random number generation, and in particular relates to a random number generation method of a laser phase noise quantum random number generator. Background Art

[0002] As an important basic resource, random numbers play a vital role in cryptography, statistics, information security and other fields. The sequences generated by traditional pseudo-random number generators based on deterministic algorithms only have statistical uniformity. They are essentially deterministic sequences and are not suitable for certain application fields with high security requirements. The quantum random number generator (QRNG) extracts random number sequences by observing quantum physical processes. It is the random number generation technology that is closest to ideal true randomness so far and has a higher level of security.

[0003] According to different measurement dimensions, QRNG schemes can be divided into two categories: discrete and continuous. Compared with discrete QRNG, continuous QRNG system has advantages in signal bandwidth and generation rate. It is the most promising solution for high-speed true random number generation. It is also a research hotspot and focus in related fields at home and abroad. Its representative schemes include measuring vacuum fluctuation noise, measuring amplified spontaneous radiation noise, and measuring laser phase noise. Among them, the scheme based on measuring laser phase noise has the advantages of high generation rate and simple system structure. After more than ten years of continuous optimization, its generation rate has been significantly improved from 100Mbps to 100Gbps, and the chipization has also developed rapidly. The currently publicly reported generation rate of such chip-based QRNG has reached the Gbps level.

[0004] It is worth noting that although the performance of this type of QRNG system has been significantly improved, its implementation method is to optimize the performance of the experimental system using empirical design methods, including: 1) selecting a laser with a wider linewidth; 2) constructing an unequal-arm interferometer with a delay difference exceeding the coherence time of the laser; 3) using a detector with a higher bandwidth to increase the system sampling rate; 4) improving the data post-processing method, etc. In actual implementation, it is necessary to build a variety of different experimental systems based on the above method for combined experiments to obtain the parameter settings with the best performance. Therefore, its essence is an a posteriori optimization method with high implementation complexity and can only obtain local optimal performance. In particular, with the continuous development of related research, low cost, miniaturization, and chipization are the inevitable trends for QRNG to move towards large-scale practical applications. Therefore, this a posteriori optimization method has more obvious limitations due to its high implementation cost. The fundamental reason for this result is that the physical model of this type of QRNG system is still not accurate and perfect enough, and the constraints between the core parameters of each component and its impact on system performance are not clear enough. In existing related research, no rigorous mathematical and physical model has been established for the laser phase noise scheme to quantitatively calculate its optimal performance conditions, and therefore no a priori optimization design method for achieving the performance boundaries of such schemes has been given. Summary of the invention

[0005] The purpose of this application is to disclose a performance optimization method for a laser phase noise quantum random number generator in order to overcome the problems of the prior art. The method of this application does not require repeated experiments, has low implementation complexity and can obtain global optimal performance, has good flexibility and practicality, and is especially important for the design and implementation of chip-based QRNG based on this scheme, and can significantly promote the practical application of laser phase noise QRNG.

[0006] The purpose of this application is achieved through the following technical solutions:

[0007] A performance optimization method for a laser phase noise quantum random number generator, the performance optimization method comprising:

[0008] S1: Evaluate the output signal bandwidth of the laser phase noise quantum random number generator system, establish the relationship between the parameters of each component of the system and the output signal bandwidth of the system, and use this to guide the actual detector selection and sampling rate setting;

[0009] S2: Evaluate the minimum entropy of the laser phase noise quantum random number generator system and establish the relationship between the core parameters of each component of the laser phase noise quantum random number generator system and the minimum entropy;

[0010] S3: Based on the relationship between the parameters of the components of the laser phase noise quantum random number generator system and the sampling rate and minimum entropy, the experimental parameter selection for optimizing the performance of the laser phase noise quantum random number generator system is obtained, thereby guiding the specific experimental design and implementation.

[0011] According to a preferred embodiment, step S1 specifically includes:

[0012] S101: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, A is the coefficient related to the light field amplitude and detection conversion gain;

[0013] S102: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ;

[0014] S103: Construct a set of random variables X, whose distribution satisfies X~N(0,2πΔfT a );

[0015] S104: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is,

[0016] X={x1,x2,x3,…,x N}

[0017] Where N is a positive integer, then Y = {y1, y2, y3, ..., y n},in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal;

[0018] S105: Calculate time interval T d The phase increment Δθ(t,T d );

[0019] In step S105, let T d is the time interval between adjacent samples in the simulation a An integer multiple of T d =mT a ;

[0020] Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable z, z = {z1,z2,z3,…,z n-m}, where z i =y m+i -y i , i is a positive integer, and i≤Nm;

[0021] S106: Calculate the system output signal Q, that is, Q=A sin Z;

[0022] S107: Calculate the power spectrum of the signal Q and estimate its 3dB bandwidth BW;

[0023] S108: changing Δf, repeating steps S11 to S16, and obtaining the 3dB bandwidth BW of the signal Q at different Δf;

[0024] S109: Change time interval T d , repeat steps S11 to S16 to obtain different time intervals T d The 3dB bandwidth BW of the signal Q when

[0025] S110: Draw the curves of BW changing with Δf C1 and BW changing with T d The change curve C2;

[0026] S111: Based on C1 and C2 and the Nyquist theorem, the sampling rate of the analog-to-digital converter in the system can be set to f s Therefore, we can get f s Curve C with the change of Δf 1f and f s Follow T d The change curve C 2f .

[0027] According to a preferred embodiment, in step S101, A=1 during simulation; in step S103, the sample size of random variable X is not less than 10 6 In step S104, N≥10 6 .

[0028] According to a preferred embodiment, in step S2, the system minimum entropy evaluation method includes:

[0029] S201: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, A is the coefficient related to the light field amplitude and detection conversion gain;

[0030] S202: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ;

[0031] S203: Construct a set of random variables X whose distribution satisfies X~N(0,2πΔfT a );

[0032] S204: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is,

[0033] X={x1,x2,x3,…,x N}

[0034] Where N is a positive integer, then Y = {y1, y2, y3, ..., y n},in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal;

[0035] S205: Calculate time interval T d The phase increment Δθ(t,T d );

[0036] In step S205, let T d is the time interval between adjacent samples in the simulation a An integer multiple of T d =mT a ;

[0037] Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable Z, Z = {z1,z2,z3,…,z n-m}, where z i =y m+i -y i , i is a positive integer, and i≤Nm;

[0038] S206: Calculate the system output signal Q, that is, Q = AsinZ;

[0039] S207: Calculate the minimum entropy H of Q min ;

[0040] S208: Change Δf, repeat steps S21 to S27, and obtain H under different Δf min ;

[0041] S209: Change T d Repeat steps S21 to S27 to obtain different T d H min ;

[0042] S210: Draw H min Curves C3 and H with the change of Δf min Follow T d The change curve C4.

[0043] According to a preferred embodiment, in step S201, A=1 during simulation; in step S203, the sample size of random variable X is not less than 10 6 In step S204, N ≥ 106 .

[0044] According to a preferred embodiment, step S3 includes:

[0045] S301. Based on the C obtained in step S1 1g and C3 obtained in step S2, can be calculated based on R=f s ×H min The random number generation rate R is obtained as a function of Δf.

[0046] S302. Based on the C obtained in step S1 2f and C4 obtained in step S2, can be calculated based on R=f s ×H min Get the random number generation rate R with T d The change curve L2;

[0047] S303. According to the specific experimental conditions, based on the L1 and L2 curves in step S301 and step S302, select the laser line width Δf and the delay difference T in the unequal arm interferometer d , so that the system random number generation rate reaches the optimal value.

[0048] The aforementioned main scheme of the present application and its further options can be freely combined to form multiple schemes, all of which are schemes that can be adopted and claimed for protection in the present application. After understanding the scheme of the present application, those skilled in the art can understand that there are multiple combinations based on the prior art and common knowledge, all of which are technical schemes to be protected by the present application, and they are not exhaustively listed here.

[0049] Beneficial effects of this application:

[0050] (1) This application not only provides a physical model of the relationship between the core parameters of a quantum random number generator system based on laser phase noise, but also provides a quantitative evaluation model of the impact of the core parameters of each component on system performance based on this model, which innovates the performance evaluation method of this type of random number generator in existing research;

[0051] (2) Using the physical model proposed in this application, simulation analysis can be carried out a priori to guide experimental design to obtain the best performing QRNG system;

[0052] (3) Based on the performance optimization method for a single continuous laser phase noise quantum random generator proposed in this application, a high generation rate can be achieved by optimizing the configuration under existing experimental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a schematic diagram of the random number generation process of a typical laser phase noise QRNG technology solution. DETAILED DESCRIPTION

[0054] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0055] Since the random phase fluctuation of the light field originates from the spontaneous radiation effect inside the laser, it is a typical quantum physics process with significant randomness. At the same time, the phase of the light field cannot be measured directly. Therefore, in the typical QRNG scheme for measuring laser phase noise, an unequal-arm interferometer is constructed to detect and extract the random phase fluctuation of the laser light field. The quantum random number generation process is as follows: Figure 1 As shown, it includes four steps: quantum state preparation, quantum state detection, quantum noise signal acquisition and quantization, and data post-processing.

[0056] In the typical QRNG technology scheme based on laser phase noise, in the first quantum state preparation stage, in order to avoid introducing classical phase noise related to the environment, phase compensation measures are usually used to control the phase drift introduced by the unequal arm interferometer due to environmental changes. In the second quantum state detection stage, the output electrical signal of the interference light signal after phase drift compensation after passing through the photodetector can be expressed as

[0057] V=A sin(Δθ(t,T d ))

[0058] Where A is the comprehensive gain coefficient related to the laser light field amplitude, the attenuation value of the unequal-arm interferometer, the internal beam splitting ratio, the detector gain conversion coefficient, etc. d )=θ(t)-θ(tL d ) is the laser at time interval T d The phase variation within the laser comes from the spontaneous emission inside the laser and is the fundamental source of randomness for this type of QRNG.

[0059] In step three, quantum noise sampling and quantization, an analog-to-digital converter is used to sample at a certain sampling frequency f s Discrete sampling is performed on the detection output electrical signal to obtain the original random sequence.

[0060] In step 4, the data post-processing stage is divided into two sub-steps: entropy evaluation and randomness extraction. Entropy evaluation refers to the calculation of the system minimum entropy of the original random sequence obtained in step 3 according to the definition of minimum entropy. The key parameter for measuring the lower bound of randomness that can be extracted from the sample is the system minimum entropy. Accurate entropy evaluation is the basis for ensuring that the actual QRNG system has provable security. Given a probability distribution P[X=x i ], its minimum entropy is defined as

[0061]

[0062] Randomness extraction refers to designing a randomness extraction algorithm based on the minimum entropy evaluation results, compressing the original random sequence, and finally obtaining quantum true random number output.

[0063] The above are the main steps of the quantum random number generation process for measuring laser phase noise QRNG scheme. Generally speaking, without oversampling, the core performance parameter of the QRNG system - random number generation rate R - is equal to the sampling rate f of the analog-to-digital converter. s Multiply by the minimum entropy H of each sample min , that is, R = f s ×H min Obviously, by increasing the sampling rate of the analog-to-digital converter, s And the minimum entropy of the system H min It can improve the performance of QRNG system. In the existing QRNG system for measuring laser phase noise, f s It is necessary to combine the spectrum of the collected data and the calculation results of the autocorrelation coefficient to determine, H min It is necessary to evaluate the statistical distribution of the collected data, so by increasing f s and H min The method to improve the performance of the QRNG system is a posteriori and requires repeated experiments. This a posteriori method has two problems. First, the setting of the sampling rate may be contrary to the principle generally followed in existing research, that is, the sampling rate f s The first is that the bandwidth BW of the signal to be measured should be determined, and it should be set to no more than one or two times the signal bandwidth BW; the second is that the efficiency from system design to implementation is low, and the system performance can only reach the local optimum. The fundamental reason for this result is that the physical model of this type of QRNG system is still not accurate enough, and the constraints between the core parameters of each component and its impact on system performance are not clear enough.

[0064] In response to the above problems, this application constructs a more complete mathematical model for this type of QRNG scheme, which can quantify the impact of the core parameters of the system components on the system performance, so as to a priori guide the actual system design and obtain the optimal QRNG experimental system. In this application, three core steps are required to achieve the optimal performance of the laser phase noise quantum random number generator system: S1: Evaluate the system output signal bandwidth, establish the relationship between the core parameters of each component of the system and the system output signal bandwidth, and guide the actual detector selection and sampling rate setting accordingly; S2: Evaluate the system minimum entropy, establish the relationship between the core parameters of each component of the system and the minimum entropy; S3: Combine the relationship between the system component parameters and the sampling rate and minimum entropy, and optimize the design to achieve the best system performance.

[0065] Specifically, the phase of the laser output optical signal can be modeled by the Wiener process (also known as the Brownian motion process), that is, at any time interval T d The phase increment Δθ(t,T d ) has a mean of 0 and a variance of 2πΔfT d Normal distribution, where Δf is the laser line width. Therefore, in determining the QRNG system laser line width Δf and the delay difference T of the unequal arm interferometer d After that, we can use numerical methods to construct Δθ(t,T d ) thus detecting the output electrical signal V = A sin (Δθ (t, T d )) is simulated, and based on the simulation results, time domain statistical characteristics analysis and frequency domain power spectrum analysis can be carried out to obtain the signal bandwidth BW and minimum entropy H, which are directly related to the random number generation rate. min On this basis, Δf and T can be changed d Repeat the above simulation calculation to establish BW and H min With Δf and T d The change relationship curve of the system performance is finally obtained, and the experimental parameter selection with the best system performance is guided to the specific experimental design and implementation. The details are as follows.

[0066] The method for evaluating the bandwidth of the system output signal in step S1 includes:

[0067] Step S101: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, and A is a coefficient related to the light field amplitude and the detection conversion gain; in step S11, A=1 during simulation.

[0068] Step S102: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ;

[0069] Step S103: construct a set of random variables X, whose distribution satisfies X~N(0,2πΔfT a ); Preferably, the sample size of random variable X is not less than 10 6 .

[0070] Step S104: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is,

[0071] X={x1,x2,x3,…,x N}

[0072] Where N is a positive integer, N ≥ 10 6 Then Y={y1,y2,y3,…,y n},in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal;

[0073] Step S105: Calculate the time interval T d The phase increment Δθ(t,T d );

[0074] In step S105, let T d is the time interval between adjacent samples in the simulation da An integer multiple of T d =mT a ;

[0075] Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable Z, Z = {z1,z2,z3,…,z n-m}, where z i =y m+i -y i , i is a positive integer, and i≤Nm;

[0076] Step S106: Calculate the system output signal Q, that is, Q=AsinZ;

[0077] Step S107: Calculate the power spectrum of the signal Q and estimate its 3dB bandwidth BW;

[0078] Step S108: changing Δf, repeating steps S11 to S16, and obtaining the 3dB bandwidth BW of the signal Q at different Δf;

[0079] Step S109: Change the time interval T d , repeat steps S11 to S16 to obtain different time intervals T d The 3dB bandwidth BW of the signal Q when

[0080] Step S110: Plot the curve C1 of BW changing with Δf and the curve of BW changing with T d The change curve C2;

[0081] Step S111: According to C1 and C2 and Nyquist's theorem, the sampling rate of the analog-to-digital converter in the system can be set to f s Therefore, we can get f s Curve C with the change of Δf 1f and f s Follow T d The change curve C 2f .

[0082] Preferably, in step S2, the system minimum entropy evaluation method includes:

[0083] S201: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, A is a coefficient related to the light field amplitude and detection conversion gain; A=1 in simulation.

[0084] S202: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ;

[0085] S203: Construct a set of random variables X whose distribution satisfies X~N(0,2πΔfT a ); In step S23, the sample size of random variable X is not less than 10 6 .

[0086] S204: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is,

[0087] X={x1,x2,x3,…,x N}

[0088] Where N is a positive integer, N ≥ 10 6 Then Y={y1,y2,y3,…,y n},in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal;

[0089] S205: Calculate time interval T d The phase increment Δθ(t,T d );

[0090] In step S205, let T d is the time interval between adjacent samples in the simulation d An integer multiple of T d =mTa ;

[0091] Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable Z, Z = {z1,z2,z3,…,z n-m}, where z i =y m+i -y i , i is a positive integer, and i≤Nm;

[0092] S206: Calculate the system output signal Q, that is, Q = A sin Z;

[0093] S207: Calculate the minimum entropy H of Q min ;

[0094] S208: Change Δf, repeat steps S21 to S27, and obtain H under different Δf min ;

[0095] S209: Change T d Repeat steps S21 to S27 to obtain different T d H min ;

[0096] S210: Draw H min Curves C3 and H with the change of Δf min Follow T d The change curve C4.

[0097] Step S3 includes:

[0098] S301. Based on the C obtained in step S1 1f and C3 obtained in step S2, can be obtained according to R=f s ×H min The random number generation rate R is obtained as a function of Δf.

[0099] S302. Based on the C obtained in step S1 2f and C4 obtained in step S2, can be calculated based on R=f s ×H min Get the random number generation rate R with T d The change curve L2;

[0100] S303. According to the specific experimental conditions, based on the L1 and L2 curves in step S301 and step S302, select the laser line width Δf and the delay difference T in the unequal arm interferometer d , so that the system random number generation rate reaches the optimal value.

[0101] This application not only provides a physical model of the relationship between the core parameters of a quantum random number generator system based on laser phase noise, but also provides a quantitative evaluation model of the impact of the core parameters of each component on system performance based on the model, which innovates the performance evaluation method for this type of random number generator in existing research; using the physical model proposed in this application, simulation analysis can be carried out a priori to guide experimental design to obtain the QRNG system with the best performance; based on the performance optimization method for a single continuous laser phase noise quantum random number generator proposed in this application, a high generation rate can be achieved through optimized configuration under existing experimental conditions.

[0102] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A performance optimization method for a laser phase noise quantum random number generator, characterized in that: The performance optimization method comprises: S1: Evaluate the output signal bandwidth of the laser phase noise quantum random number generator system, establish the relationship between the parameters of each component of the system and the output signal bandwidth of the system, and use this to guide the actual detector selection and sampling rate setting; S2: Evaluate the minimum entropy of the laser phase noise quantum random number generator system and establish the relationship between the core parameters of each component of the laser phase noise quantum random number generator system and the minimum entropy; S3: Based on the relationship between the parameters of the components of the laser phase noise quantum random number generator system and the sampling rate and minimum entropy, the experimental parameter selection for optimizing the performance of the laser phase noise quantum random number generator system is obtained, thereby guiding the specific experimental design and implementation.

2. The performance optimization method of the laser phase noise quantum random number generator according to claim 1, characterized in that: Step S1 specifically includes: S101: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, A is the coefficient related to the light field amplitude and detection conversion gain; S102: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ; S103: Construct a set of random variables X, whose distribution satisfies X~N(0,2πΔfT a ); S104: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is, X={x1,x2,x3,…,x N } Where N is a positive integer, then Y = {y1, y2, y3, ..., y n },in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal; S105: Calculate time interval T d The phase increment Δθ(t,T d ); In step S105, let T d is the time interval between adjacent samples in the simulation a An integer multiple of T d =mT a ; Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable Z, Z = {z1,z2,z3,…,z n-m }, where z i =y m+i -y i , i is a positive integer, and i≤Nm; S106: Calculate the system output signal Q, that is, Q=A sin Z; S107: Calculate the power spectrum of the signal Q and estimate its 3dB bandwidth BW; S108: changing Δf, repeating steps S11 to S16, and obtaining the 3dB bandwidth BW of the signal Q at different Δf; S109: Change time interval T d , repeat steps S11 to S16 to obtain different time intervals T d The 3dB bandwidth BW of the signal Q when S110: Draw the curves of BW changing with Δf C1 and BW changing with T d The change curve C2; S111: Based on C1 and C2 and the Nyquist theorem, the sampling rate of the analog-to-digital converter in the system can be set to f s Therefore, we can get f s Curve C with the change of Δf 1f and f s Follow T d The change curve C 2f .

3. The performance optimization method of the laser phase noise quantum random number generator according to claim 1, characterized in that: In step S101, A=1 during simulation; in step S103, the sample size of random variable X is not less than 10 6 In step S104, N ≥ 10 6 .

4. The performance optimization method of the laser phase noise quantum random number generator according to claim 2, characterized in that: In step S2, the system minimum entropy evaluation method includes: S201: Select the initial parameters of the laser phase noise quantum random number generator system, Δf is the laser line width, T d is the delay difference in the unequal-arm interferometer uMZI, A is the coefficient related to the light field amplitude and detection conversion gain; S202: Set a time interval parameter representing the time interval between two adjacent samples, denoted as T a ; S203: Construct a set of random variables X whose distribution satisfies X~N(0,2πΔfT a ); S204: Use the cumulative function to simulate the Wiener process to obtain a new random variable Y, that is, X={x1,x2,x3,…,x N } Where N is a positive integer, then Y = {y1, y2, y3, ..., y n },in i is a positive integer and i≤N; at this time, Y is the phase random variable θ(t) of the laser output optical signal; S205: Calculate time interval T d The phase increment Δθ(t,T d ); In step S205, let T d is the time interval between adjacent samples in the simulation a An integer multiple of T d =mT a ; Therefore, the laser outputs a light signal at a time interval T d The phase increment Δθ(t,T d ) can be represented by a random variable Z, Z = {z1,z2,z3,…,z n-m }, where z i =y m+i -y i , i is a positive integer, and i≤Nm; S206: Calculate the system output signal Q, that is, Q = A sin Z; S207: Calculate the minimum entropy H of Q min ; S208: Change Δf, repeat steps S21 to S27, and obtain H under different Δf min ; S209: Change T d Repeat steps S21 to S27 to obtain different T d H min ; S210: Draw H min Curves C3 and H with the change of Δf min Follow T d The change curve C4.

5. The performance optimization method of the laser phase noise quantum random number generator according to claim 4, characterized in that: In step S201, the simulation is set to A = 1; in step S203, the sample size of the random variable X is not less than 10 6 In step S204, N ≥ 10 6 .

6. The performance optimization method of the laser phase noise quantum random number generator according to claim 4, characterized in that: Step S3 includes: S301. Based on the C obtained in step S1 1g and C3 obtained in step S2, can be calculated based on R=f s ×H min The random number generation rate R is obtained as a function of Δf. S302. Based on the C obtained in step S1 2f and C4 obtained in step S2, can be calculated based on R=f s ×H min Get the random number generation rate R with T d The change curve L2; S303. According to the specific experimental conditions, based on the L1 and L2 curves in step S301 and step S302, select the laser line width Δf and the delay difference T in the unequal arm interferometer d , so that the system random number generation rate reaches the optimal value.

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