Entropy polarization-based quantum random number generator data post-processing method and device, equipment and medium

Abstract

The present application relates to the technical field of quantum communication, and provides a quantum random number generator data post-processing method and device based on entropy polarization, equipment and medium, the method comprises the following steps: generating a continuous analog electrical signal; sampling the continuous analog electrical signal to obtain an original random number sequence; constructing a bit matrix based on the original random number sequence; polarizing the bit matrix; estimating the conditional entropy of the polarized bit matrix; selecting a high-entropy index set according to the conditional entropy; and obtaining a final random sequence according to the high-entropy index set. The present application uses entropy polarization technology, which is significantly different from the traditional post-processing method that relies on random seeds. The polarization matrix dimension is determined by the length of the input sequence, and the randomness can be efficiently extracted without the participation of external random seeds, which theoretically eliminates the security risks and resource burdens caused by the introduction of random seeds.

Description

Technical Field

[0001] This invention relates to the field of quantum communication technology, and more specifically, to a method, apparatus, device, and medium for post-processing data from a quantum random number generator based on entropy polarization. Background Technology

[0002] In the field of modern information technology, random numbers are an indispensable and crucial resource, playing a vital role in several key areas such as cryptography, statistics, and scientific simulation. For example, in cryptographic applications, the effectiveness and security of secure communication protocols such as encryption, digital signatures, and authentication highly depend on high-quality random numbers. However, traditional pseudo-random number generators (PRNGs) generate random number sequences based on deterministic algorithms and initial seeds. These generated random numbers may exhibit certain regularities or predictability under specific conditions, thus posing potential security risks. Especially when facing adversaries with attack capabilities, the random numbers generated by PRNGs may be predictable or reproducible, thereby compromising system security.

[0003] Quantum Random Number Generator (QRNG), as an emerging technology, extracts quantum random numbers by observing quantum physical processes, providing a random number generation scheme based on quantum physics principles. Unlike classical pseudo-random number generation methods, QRNG-generated random numbers originate from the inherent uncertainty of quantum mechanics, and theoretically, their results are unpredictable. It is the closest random number generation technology to ideal true randomness to date, thus exhibiting significant advantages in applications with extremely high security requirements.

[0004] A typical QRNG system usually consists of core components such as a quantum entropy source (QES), a sampling module, a post-processing module, and a randomness detection module, such as... Figure 1As shown, the quantum entropy source consists of a quantum light source and a detection module. The quantum light source generates quantum state signals with true randomness, and the detection module converts these signals into analog electrical signals. The sampling module discretizes the analog electrical signals to generate an initial random sequence. Next, the post-processing module further processes the generated initial random sequence to remove deviations or non-uniformities that may be caused by classical noise or systematic errors, ensuring that the final generated random sequence meets strict randomness requirements. Finally, the randomness detection module performs comprehensive statistical analysis and verification on the final generated random sequence to ensure that it meets international / domestic standards or industry specifications, further improving the usability and reliability of the random numbers. This randomness detection module typically relies on a series of standard randomness tests, such as NIST SP800-22 and GM / T 0005-2021, to ensure that the system's output random numbers can be used safely and efficiently in different application scenarios.

[0005] Post-processing in QRNG systems can be implemented using various methods, primarily including: lightweight post-processing methods such as von Neumann bias correction, least significant bit (m-LSB) methods, and XOR methods; strong randomness extractor methods such as Toeplitz matrices and Trevisan matrices; and post-processing methods based on cryptographic functions such as hash functions and block ciphers. Each of these methods has its own characteristics, and in practical applications, a comprehensive evaluation based on factors such as security, efficiency, cost, and compliance is necessary to achieve the optimal balance between cost, performance, and security.

[0006] Among existing QRNG random number post-processing schemes, the Toeplitz matrix method is widely used due to its simple structure and provable information-theoretic security. However, this method is essentially a random seed-assisted randomness extractor, which relies on a length of... m + n A high-quality random seed of -1 is used to initialize the Toeplitz matrix, where n For the input length, m This is the output length. For a length of... n The original random number sequence, which is related to m × n Multiplying the Toeplitz matrices will output a matrix of length 1. m The output random sequence is calculated using the following formula:

[0007] The time complexity of multiplying the Toeplitz matrix and the input vector without optimization is as high as [missing value]. Even with the Fast Fourier Transform (FFT) for acceleration, its theoretical optimal computational complexity is still O(n log n). For high-speed QRNG systems, this type of computation is costly and difficult to implement, and may become a performance bottleneck for the overall system. Furthermore, the use of random seeds can lead to resource waste and security vulnerabilities. To achieve strict information-theoretic security, the random seeds used in this method must be independent of the original random sequence of input and follow a uniform distribution; otherwise, the security and unpredictability of the output bits will be affected. Simultaneously, to avoid potential correlations and structural leaks, random seeds are, in principle, non-reusable. This means that to achieve ideal information-theoretic security, a new seed sequence needs to be generated for each round of processing, increasing resource consumption. In practical deployments, if multiple post-processing modules share the same seed or reuse the same seed, attackers may analyze the correlations between multiple outputs to infer some of the original input information or reconstruct part of the Toeplitz matrix structure, thus creating a side-channel attack risk. Such security vulnerabilities caused by seed leakage or reuse are difficult to detect in randomness detection but can become weak points in the entire QRNG system. Especially in high-security scenarios (such as quantum key distribution and military secure communications), the requirements for system structure enclosure and confidentiality are even higher, and any functional module that can be predicted or reconstructed constitutes a potential security vulnerability. Therefore, from the perspective of system security, the external random seed mechanism on which the Toeplitz matrix method relies introduces additional trust assumptions and management burdens in its design. It is not suitable as a general seedless randomness extraction method in high-security and high-confidentiality application scenarios. Summary of the Invention

[0008] This invention aims to provide a data post-processing method, apparatus, device, and medium based on entropy polarization quantum random number generators, in order to solve the problems of resource waste, security vulnerabilities, and relatively high computational complexity caused by the use of seeds in commonly used post-processing methods in current QRNG systems.

[0009] In a first aspect, the present invention provides a data post-processing method for a quantum random number generator based on entropy polarization, comprising: Generate continuous analog electrical signals; A continuous analog electrical signal is sampled to obtain an original random number sequence; Construct a bit matrix based on the original random number sequence; Polarize the bit matrix; Estimate the conditional entropy of the polarized bit matrix; Select a high-entropy index set based on conditional entropy; The final random sequence is obtained from the high-entropy index set.

[0010] In a preferred embodiment, the analog electrical signal is discretized and sampled, and the original random number sequence obtained from each discretization sampling is a sequence containing... m A binary bit vector of discrete bits.

[0011] In a preferred embodiment, constructing the bit matrix based on the original random number sequence includes: continuous n After the discretization sampling, we obtain n Includes m A binary bit vector of discrete bits; Will n Arrange the binary bit vectors in the sampling order to obtain a m OK n A column-wise bit matrix.

[0012] In a preferred embodiment, the polarization of the bit matrix includes: Construct the polarization matrix; Multiply the bit matrix by the polarization matrix on the right to obtain the polarized bit matrix.

[0013] In a preferred embodiment, estimating the conditional entropy of the polarized bit matrix refers to calculating the conditional entropy for each bit in the polarized bit matrix; the calculation method employs statistical methods or structural recursive methods.

[0014] In a preferred embodiment, selecting the high-entropy index set based on conditional entropy includes: The conditional entropy of each bit in the bit matrix is ​​compared with the high entropy threshold parameter; Bits whose conditional entropy exceeds the high entropy threshold parameter are considered high-entropy bits, and their position indices are added to the high-entropy index set.

[0015] In a preferred embodiment, obtaining the final random sequence based on the high-entropy index set includes: Based on the high-entropy bit position index in the high-entropy index set, extract the high-entropy bit at the corresponding position from the polarized bit matrix.

[0016] The extracted high-entropy bits are arranged in order and spliced ​​together to form the final random sequence.

[0017] Secondly, the present invention provides a data post-processing device for a quantum random number generator based on entropy polarization, comprising: A quantum entropy source, used to output continuous analog electrical signals; The sampling module is used to sample continuous analog electrical signals to obtain the original random number sequence; The bit matrix construction module is used to construct a bit matrix based on a raw random number sequence. The polarization transformation module is used to polarize the bit matrix; The conditional entropy estimation module is used to estimate the conditional entropy of the polarized bit matrix. The high-entropy index set selection module is used to select high-entropy index sets based on conditional entropy. The final random sequence generation module is used to obtain the final random sequence based on the high-entropy index set.

[0018] Thirdly, the present invention provides an electronic device, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the above-described method.

[0019] Fourthly, the present invention provides a computer-readable storage medium for storing instructions that, when executed, cause the above-described method to be implemented.

[0020] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This invention utilizes entropy polarization technology, significantly differentiating itself from traditional post-processing methods that rely on random seeds. Its polarization matrix dimension is determined by the length of the input sequence, enabling efficient randomness extraction without the need for an external random seed. Theoretically, this eliminates the security risks and resource burdens associated with introducing random seeds. Furthermore, the polarization matrix structure used in this invention is fixed, thus it can, in principle, be directly embedded into hardware logic with high execution efficiency. Compared to traditional post-processing methods commonly used in QRNG systems, this invention combines information-theoretic security and provability with significant feasibility, providing a high-speed, high-security, and low-cost post-processing solution for QRNG systems. Attached Figure Description

[0021] Figure 1 This is a diagram of the QRNG system architecture.

[0022] Figure 2 This is a schematic diagram of polarization.

[0023] Figure 3 This is a diagram showing the conditional entropy distribution.

[0024] Figure 4 The flowchart below provides a data post-processing method for a quantum random number generator based on entropy polarization, as an embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of the structure of a data post-processing device for a quantum random number generator based on entropy polarization, provided in an embodiment of the present invention.

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] To address the resource waste, security vulnerabilities, and relatively high computational complexity associated with commonly used post-processing methods in current QRNG systems that rely on seeds, this invention provides a post-processing method for quantum random number generator data based on entropy polarization. This method avoids dependence on external random seeds while also possessing effective randomness extraction capabilities and information-theoretic security, thereby significantly improving the security and reliability of quantum random number generator systems.

[0030] The underlying theory upon which this invention is based is the phenomenon of entropy polarization, a mechanism derived from polar codes in information theory and widely used in the field of communication.

[0031] Polar codes are a channel coding method proposed by Erdal Arikan in 2009. They are based on the phenomenon of channel polarization to construct efficient coding schemes. The design concept of polar codes is to polarize multiple parallel channels into two types of channels through channel transformation: one type of channel has a bit error rate close to 0 (high reliability), and the other type of channel has a bit error rate close to 1 (low reliability).

[0032] The encoding process of polar codes relies on recursive polarization transformations, typically implemented using a transformation matrix. Assume the input bit sequence is:

[0033] in, It is the length of the codeword; polar codes are obtained through a matrix. Transform the information bits. The transformation matrix is... It is composed of the basic polarization transformation matrix. F It is composed of its Kronecker product, in the following form:

[0034] in, It is the basic polarization transformation matrix. Represents the polarization transformation matrix F n Kronecker product, It is a bit-reversed permutation matrix. Then, matrix multiplication is performed to obtain the polarized output:

[0035] When the length of the codeword Input bit sequence At that time, polarization transformation is as follows Figure 2 As shown.

[0036] After the concept of polar codes was proposed, this research was further expanded to a wider range of situations, one of which is entropy polarization. The core issue addressed by entropy polarization is not the reliability of channel transmission, but rather the internal entropy structure of the input source. It describes the characteristic that the conditional entropies in the chain rule gradually differentiate towards extreme values ​​during polarization transformation. Let... and Let these represent the Shannon entropy of the input and the output sequences after polarization, respectively. Since polarization is a linear transformation, it satisfies the entropy conservation relation:

[0037] According to the chain rule of entropy, for Then we have:

[0038] Define the distribution function of conditional entropy. Defined as:

[0039] in, It is the output sequence number The conditional entropy of a position. According to entropy polarization theory, as the scale of the transformation... The increase, Converges to the step function:

[0040] The results of this formula show that, after polarization transformation, the conditional entropies in the chain rule exhibit bipolarity in the limiting sense, approximately... The conditional entropy of the proportion is close to 0, approximately The conditional entropy of the ratio is close to 1, thus characterizing the core feature of the entropy polarization phenomenon. Figure 3A specific example of the conditional entropy distribution is given. Figure 3 In the scatter representation of the chain rule, the output sequence of the first... k The conditional entropy of a bit The distribution shows that the conditional entropy value gradually tends to the two extremes of 0 or 1, exhibiting the characteristics of entropy polarization.

[0041] Entropy polarization mechanisms possess important information-theoretic properties. On one hand, they do not rely on the assumption of independent and identically distributed input variables, making them applicable to non-independent models, especially demonstrating good adaptability when dealing with sources exhibiting local correlation or block-structure dependence. On the other hand, since the transformation is inherently a linearly reversible operation, the polarization process maintains low computational complexity. Its advantages make it suitable for large-scale data processing and hardware acceleration.

[0042] Furthermore, existing literature has proven that: It is a power of 2. It is A random bit matrix, wherein each column is independent and identically distributed, and takes values ​​from . random variable (distributed as) ),make ,in This is the polarization matrix. Therefore, for any 0... Both have two disjoint sets of indices. and ,satisfy: In the set Above is ,and .in, express In the index set The statistical distance between the sequence and the ideal independent and identically distributed uniform random sequence does not exceed The method for selecting high-entropy index sets provided by entropy polarization theory offers a theoretical basis for post-processing in quantum random number generators.

[0043] Based on the above principles, such as Figure 4 As shown, this embodiment of the invention provides a data post-processing method for a quantum random number generator based on entropy polarization, including: S100, prepare the original random number sequence; S200, a bit matrix is ​​constructed based on the original random number sequence; S300, polarizes the bit matrix; S400, estimate the conditional entropy of the polarized bit matrix; S500 selects a high-entropy index set based on conditional entropy; S600, the final random sequence is obtained based on the high-entropy index set.

[0044] Therefore, this invention utilizes entropy polarization technology, significantly differentiating itself from traditional post-processing methods that rely on random seeds. Its polarization matrix dimension is determined by the length of the input sequence, enabling efficient randomness extraction without the need for an external random seed, theoretically eliminating the security risks and resource burdens associated with introducing random seeds. Furthermore, the polarization matrix structure used in this invention is fixed, thus it can, in principle, be directly embedded into hardware logic with high execution efficiency. Compared to traditional post-processing methods commonly used in QRNG systems, this invention combines information-theoretic security and provability with significant feasibility, providing a high-speed, high-security, and low-cost post-processing solution for QRNG systems.

[0045] The following details the specific implementation of the entropy-polarized quantum random number generator data post-processing method.

[0046] S100 generates a continuous analog electrical signal: S101, the quantum light source generates quantum state signals; specifically, the quantum entropy source of the quantum random number generator uses quantum physical phenomena (such as ASE light source, vacuum state fluctuations, etc.) to extract randomness, thereby generating quantum state signals with true randomness through quantum effects.

[0047] S102, the detection module outputs a continuous analog electrical signal after detecting the quantum state signal; S200 samples a continuous analog electrical signal to obtain the original random number sequence.

[0048] A sampling module is used to discretize the analog electrical signal. This sampling module can be implemented using an m-bit analog-to-digital converter (ADC). When the continuous analog electrical signal is converted into a discrete signal by the m-bit ADC, the original random number sequence obtained from each discretization sampling is a sequence containing... m A binary bit vector of discrete bits is represented as:

[0049] S300, constructing a bit matrix based on the original random number sequence: Assumption n Powers of 2, consecutive n After the discretization sampling, we obtain n Includes m A binary bit vector of discrete bits: ,Will n Arrange the binary bit vectors in the sampling order to obtain a m OK n The column-wise bit matrix is ​​represented as:

[0050] Among them, the bit matrix X Each column corresponds to a binary bit vector. m Discrete bits, bit matrix X Each row represents a bit vector obtained from consecutive sampling, and these bit vectors are arranged in the sampling order. This mapping method of "one sample corresponds to one column" ensures that the sampled data is structurally compatible with the matrix, providing favorable structural characteristics for subsequent processing.

[0051] S400, polarizes the bit matrix: S401, Construct the polarization matrix. In this embodiment of the invention, the polarization matrix is ​​constructed using the Kronecker product, as follows:

[0052] S402, right multiplication of the bit matrix The polarized bit matrix is ​​obtained, represented as:

[0053] This step involves polarizing the polarization matrix to divide the bit matrix into high-reliability and low-reliability parts. This polarization of the bit matrix improves bit reliability.

[0054] S500 estimates the conditional entropy of the polarized bit matrix.

[0055] The randomness and reliability of bits are assessed by estimating the conditional entropy. Specifically, this is done on the polarized bit matrix. Each bit in Calculate the conditional entropy, expressed as:

[0056] In this embodiment of the invention, typical estimation methods for calculating conditional entropy include: (1) Statistical methods: The statistical methods described herein estimate the joint probability distribution through frequency statistical distribution, and then calculate the conditional entropy using methods such as Maximum Likelihood Estimate (MLE). Specifically, by performing frequency analysis on the data, the conditional probability of each pair is calculated. Then, substitute the values ​​into the entropy formula to estimate the entropy.

[0057] (2) Structural Recursive Method: This method utilizes the recursive properties of the polarization matrix to estimate conditional entropy through hierarchical recursion. In this method, it is not necessary to explicitly model the joint probability distribution; instead, it relies on the recursive structure of the polarization matrix to progressively calculate the entropy of each bit given the prior bits. Common techniques include using metrics similar to the Bhattacharyya parameter to quickly estimate the conditional entropy through recursion and approximation. This method is computationally more efficient and suitable for processing large-scale data.

[0058] S600 selects a high-entropy index set based on conditional entropy. That is, it selects high-entropy bit positions based on the estimated conditional entropy. Specifically: S601, compare the conditional entropy of each bit in the bit matrix with the high-entropy threshold parameter; the set range of the high-entropy threshold parameter is... The value should be adjusted according to the specific security requirements of the application, and can generally be set to [value]. Between; when the security requirement is higher, the high entropy threshold parameter The lower the value should be.

[0059] S602, bits whose conditional entropy exceeds the high entropy threshold parameter are considered high-entropy bits, and their position indices are added to the high-entropy index set. This results in the high-entropy index set. It refers to a set of conditions that are met. The set of high-entropy bit position indices. That is, for those satisfying Bits that are not high-entropy are considered high-entropy bits, and their position indices are added to the high-entropy index set. middle.

[0060] S700, the final random sequence is obtained based on the high-entropy index set: S701, according to the high-entropy index set The high-entropy bit position index in the polarized bit matrix Extract the high-entropy bits at the corresponding positions.

[0061] S702, the extracted high-entropy bits are arranged in order and concatenated into a final random sequence, represented as:

[0062] in, This is the final random sequence.

[0063] Based on the same technological concept, such as Figure 5 As shown, this embodiment of the invention also proposes a data post-processing device for a quantum random number generator based on entropy polarization, comprising: A quantum entropy source, used to output continuous analog electrical signals; The sampling module is used to sample continuous analog electrical signals to obtain the original random number sequence; The bit matrix construction module is used to construct a bit matrix based on a raw random number sequence. The polarization transformation module is used to polarize the bit matrix; The conditional entropy estimation module is used to estimate the conditional entropy of the polarized bit matrix. The high-entropy index set selection module is used to select high-entropy index sets based on conditional entropy. The final random sequence generation module is used to obtain the final random sequence based on the high-entropy index set.

[0064] The working principles of the above-mentioned functional modules can be referred to the description in the foregoing method embodiments, and will not be repeated here.

[0065] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the data post-processing method for a quantum random number generator based on entropy polarization provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic devices. Figure 6 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 6 The example used is the connection between the processor and memory via a bus. The bus... Figure 6 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 6 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0066] In this embodiment of the invention, the memory stores instructions that can be executed by at least one processor. By executing the instructions stored in the memory, at least one processor can execute the entropy polarization-based quantum random number generator data post-processing method described above.

[0067] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0068] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0069] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the entropy-polarized quantum random number generator data post-processing method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0070] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0071] By designing and programming the processor, the code corresponding to the entropy-polarized quantum random number generator data post-processing method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0072] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a data post-processing method for a quantum random number generator based on entropy polarization as described above.

[0073] In some alternative embodiments, the present invention also provides a aspect of a method for post-processing data of a quantum random number generator based on entropy polarization, which can also be implemented as a program product comprising program code that, when the program product is run on a device, causes the control device to perform the steps in a method for post-processing data of a quantum random number generator based on entropy polarization according to various exemplary embodiments of the present invention as described above.

[0074] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0075] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0077] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0078] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data post-processing method for a quantum random number generator based on entropy polarization, characterized in that, include: Generate continuous analog electrical signals; A continuous analog electrical signal is sampled to obtain an original random number sequence; Construct a bit matrix based on the original random number sequence; Polarize the bit matrix; Estimate the conditional entropy of the polarized bit matrix; Select a high-entropy index set based on conditional entropy; The final random sequence is obtained from the high-entropy index set.

2. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 1, characterized in that, The analog electrical signal is discretized and sampled. Each discretization sampling yields an original random number sequence containing... m A binary bit vector of discrete bits.

3. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 2, characterized in that, The construction of the bit matrix based on the original random number sequence includes: continuous n After the discretization sampling, we obtain n Includes m A binary bit vector of discrete bits; Will n Arrange the binary bit vectors in the sampling order to obtain a m OK n A column-wise bit matrix.

4. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 1, characterized in that, The polarization of the bit matrix includes: Construct the polarization matrix; Multiply the bit matrix by the polarization matrix on the right to obtain the polarized bit matrix.

5. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 1, characterized in that, The estimation of conditional entropy for the polarized bit matrix refers to calculating the conditional entropy for each bit in the polarized bit matrix; the calculation method adopts statistical methods or structural recursive methods.

6. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 5, characterized in that, The selection of high-entropy index sets based on conditional entropy includes: The conditional entropy of each bit in the bit matrix is ​​compared with the high entropy threshold parameter; Bits whose conditional entropy exceeds the high entropy threshold parameter are considered high-entropy bits, and their position indices are added to the high-entropy index set.

7. The data post-processing method for a quantum random number generator based on entropy polarization according to claim 6, characterized in that, The process of obtaining the final random sequence based on the high-entropy index set includes: Based on the high-entropy bit position index in the high-entropy index set, extract the high-entropy bit at the corresponding position from the polarized bit matrix. The extracted high-entropy bits are arranged in order and spliced ​​together to form the final random sequence.

8. A data post-processing device for a quantum random number generator based on entropy polarization, characterized in that, include: A quantum entropy source, used to output continuous analog electrical signals; The sampling module is used to sample continuous analog electrical signals to obtain the original random number sequence; The bit matrix construction module is used to construct a bit matrix based on a raw random number sequence. The polarization transformation module is used to polarize the bit matrix; The conditional entropy estimation module is used to estimate the conditional entropy of the polarized bit matrix. The high-entropy index set selection module is used to select high-entropy index sets based on conditional entropy. The final random sequence generation module is used to obtain the final random sequence based on the high-entropy index set.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1-7 to be implemented.

Images