Gaussian white noise generator and Gaussian white noise generation method

Abstract

The present application provides a Gaussian white noise generator and a Gaussian white noise generation method. The generator includes a quantum entropy source device for generating an initial quantum random number; a bit width inversion device includes: a random extraction unit for extracting randomness from the initial quantum random number to obtain a first quantum random number; a bit width adjustment unit for selecting a target queue based on a target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with a target bit width; an inversion mapping unit for performing inversion mapping processing on the second quantum random number to obtain an intermediate quantum random number; a signal conversion device for performing format conversion on the intermediate quantum random number to obtain a Gaussian white noise signal; wherein, when a target crest factor is input, a set target bit width is determined according to the target crest factor, so that the bit width adjustment unit selects a target queue for processing according to the target bit width, thereby generating a Gaussian white noise signal.

Description

Technical Field

[0001] The present application relates to the field of secure communication technology, and more specifically, to a Gaussian white noise generator and a Gaussian white noise generation method. Background Art

[0002] Gaussian white noise is an ideal noise model with uniform frequency distribution and amplitude following Gaussian distribution. It has the simplicity of mathematical analysis and wide practical application value. It is widely used in the fields of noise modeling in communications and control systems, signal processing and filtering testing, electronic countermeasures, image and audio processing, random process modeling, etc.

[0003] One of the uses of Gaussian white noise is electronic countermeasures, mainly used for electronic interference, channel interference, and radar countermeasures. Anti-interference testing of modern communication electronic equipment has become a necessary test item, and the main type of interference is noise interference. In the field of communication channel testing and electronic countermeasures, Gaussian white noise is one of the most basic and most commonly used interference sources.

[0004] The current Gaussian white noise generator can be realized by using a physical noise source. Its original noise signal does not strictly conform to the Gaussian distribution and usually requires subsequent filtering and amplification processing to approximate white noise.

[0005] In the process of realizing the concept of the present application, it was found that the Gaussian white noise signal generated by the related technology does not strictly conform to the randomness of the Gaussian distribution, and the security and unpredictability of its Gaussian white noise generator are poor. Summary of the invention

[0006] In view of this, the present application provides a Gaussian white noise generator and a Gaussian white noise generation method.

[0007] One aspect of the present application provides a Gaussian white noise generator, comprising:

[0008] A quantum entropy source device, used for generating an initial quantum random number of a digital signal type based on the quantum entropy source;

[0009] The bit width inversion device comprises:

[0010] A random extraction unit, used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number;

[0011] A bit width adjustment unit is used to select a target queue corresponding to the target bit width based on a set target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths;

[0012] An inversion mapping unit, used to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number;

[0013] A signal conversion device, used for performing format conversion on the intermediate quantum random number to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on the target bit width;

[0014] Among them, when the target peak factor is input, the set target bit width is determined according to the target peak factor, so that the bit width adjustment unit selects the target queue corresponding to the target bit width for processing according to the target bit width, thereby generating a Gaussian white noise signal with the target peak factor.

[0015] According to an embodiment of the present application, the above-mentioned quantum entropy source device includes:

[0016] The above-mentioned quantum entropy source is used to generate an initial quantum noise signal;

[0017] The analog-to-digital converter is used to convert the initial quantum noise signal into the initial quantum random number.

[0018] According to an embodiment of the present application, the above-mentioned quantum entropy source device further includes:

[0019] The first signal amplifier is used to perform signal amplification processing on the initial quantum noise signal to obtain an amplified initial quantum noise signal.

[0020] According to an embodiment of the present application, the second quantum random number is subjected to inversion mapping processing based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, including:

[0021] Generate an intermediate standard Gaussian distribution function according to the second quantum random number, the initial standard Gaussian distribution function and the target variable;

[0022] Based on the target variable, the intermediate standard Gaussian distribution function is inverted to obtain a target standard Gaussian distribution function and an inverse cumulative distribution curve corresponding to the target standard Gaussian distribution function, wherein the inverse cumulative distribution curve includes two symmetrical curves of symmetrical distribution;

[0023] For any of the above symmetric curves, segment and equally divide the above symmetric curve using the above target geometric sequence to obtain a plurality of initial segmented curve functions;

[0024] The intermediate quantum random number is generated according to the second quantum random number and the plurality of initial piecewise curve functions.

[0025] According to an embodiment of the present application, each of the above initial piecewise curve functions has a plurality of initial polynomial coefficients.

[0026] According to an embodiment of the present application, generating the intermediate quantum random number according to the second quantum random number and the plurality of initial piecewise curve functions comprises:

[0027] Processing the second quantum random number to obtain a sign bit, an address bit, and a calculation bit, wherein the sign bit, the address bit, and the calculation bit all have corresponding bit widths;

[0028] Based on the bit width of the address bit, determining a plurality of target polynomial coefficients in the target piecewise curve function corresponding to the target bit width;

[0029] The intermediate quantum random number is generated according to the plurality of target polynomial coefficients, the calculation bit and the sign bit.

[0030] According to an embodiment of the present application, the plurality of target polynomial coefficients include a first coefficient, a second coefficient and a third coefficient.

[0031] According to an embodiment of the present application, generating the intermediate quantum random number according to the plurality of the target polynomial coefficients, the calculation bits and the sign bits includes:

[0032] Processing the third coefficient and the calculated bit by a first multiplier to generate a first intermediate bit;

[0033] Processing the second coefficient and the first intermediate bit using a first adder to generate a second intermediate bit;

[0034] Processing the calculated bit and the second intermediate bit by a second multiplier to generate a third intermediate bit;

[0035] The third intermediate bit, the first coefficient and the sign bit are processed by a second adder to generate the intermediate quantum random number.

[0036] According to an embodiment of the present application, randomness is extracted from the initial quantum random number to obtain a uniformly distributed first quantum random number, including:

[0037] Determine the minimum entropy value based on the minimum entropy theory;

[0038] The initial quantum random number is converted based on the target matrix to obtain an intermediate quantum random number;

[0039] Determine a quantum random ratio according to the intermediate quantum random number and the initial quantum random number;

[0040] When the quantum random ratio is less than the minimum entropy value, the intermediate quantum random number is determined as the first quantum random number.

[0041] According to an embodiment of the present application, the signal conversion device includes:

[0042] A digital-to-analog converter, used to convert the intermediate quantum random number into the target quantum random number signal;

[0043] Wherein, the above-mentioned signal conversion device also includes:

[0044] The second signal amplifier is used to perform signal amplification processing on the above-mentioned target quantum random number signal to obtain an amplified target quantum random number signal.

[0045] According to an embodiment of the present application, the Gaussian white noise generator further includes:

[0046] A state monitoring device is used to monitor the working state of the above-mentioned quantum entropy source, so as to adjust the working states of the above-mentioned quantum entropy source and the above-mentioned first signal amplifier according to the monitoring result.

[0047] Another aspect of the present application provides a method for generating Gaussian white noise, comprising:

[0048] Based on a quantum entropy source, an initial quantum random number of a digital signal type is generated using a quantum entropy source device;

[0049] Using a random extraction unit to randomly extract the initial quantum random number to obtain a uniformly distributed first quantum random number;

[0050] When a target crest factor is input, the target bit width is determined according to the target crest factor;

[0051] Based on the target bit width set above, a bit width adjustment unit is used to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths;

[0052] Using an inversion mapping unit to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number, wherein the bit width inversion device includes the random extraction unit, the bit width adjustment unit and the inversion mapping unit;

[0053] The intermediate quantum random number is format-converted by a signal conversion device to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on the target bit width.

[0054] According to an embodiment of the present application, an initial quantum random number of a digital signal type is generated by a quantum entropy source device including a quantum entropy source, and a random extraction unit is used to perform random extraction on the initial quantum random number to obtain a uniformly distributed first quantum random number, and then the target bit width is inferred based on the required target crest factor, and a bit width adjustment unit is used to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with a target bit width, and then an inversion mapping unit performs inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number with a Gaussian distribution, and finally a Gaussian white noise signal of an analog signal type that obeys a Gaussian distribution is obtained by performing format conversion through a signal conversion device. The quantum entropy source generates true random numbers through the random characteristics of the quantum physics system itself. The randomness of the output is guaranteed by the basic principles of quantum mechanics and has a clear source. Therefore, the Gaussian white noise signal obtained in the above manner effectively improves the security and unpredictability of the Gaussian white noise generator, and further improves the integration and stability of the system integrated with the Gaussian white noise generator. At the same time, the target bit width is inferred by the target crest factor, and the target queue is selected for processing by the bit width adjustment unit, so that a Gaussian white noise signal with a target crest factor can be obtained, thereby improving the generation accuracy of the Gaussian white noise signal. At the same time, the Gaussian white noise generator of the embodiment of the present application can generate Gaussian white noise with an adjustable crest factor, thereby having the advantage of being suitable for the use requirements of a variety of different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and other objects, features and advantages of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings, in which:

[0056] Figure 1 A structural block diagram of a Gaussian white noise generator according to an embodiment of the present application is shown;

[0057] Figure 2 A data processing flow chart of a Gaussian white noise generator according to an embodiment of the present application is shown;

[0058] Figure 3 A schematic diagram of a curve of an intermediate standard Gaussian distribution function according to an embodiment of the present application is shown;

[0059] Figure 4 A curve schematic diagram of a target standard Gaussian distribution function according to an embodiment of the present application is shown;

[0060] Figure 5 A schematic diagram of a segmented symmetric curve according to an embodiment of the present application is shown;

[0061] Figure 6 A schematic diagram of generating a Gaussian white noise signal according to an embodiment of the present application is shown;

[0062] Figure 7 A schematic diagram of generating an intermediate quantum random number according to an embodiment of the present application is shown;

[0063] Figure 8 A flowchart of a method for generating Gaussian white noise according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0064] Below, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application.

[0065] The terms used herein are only for describing specific embodiments and are not intended to limit the present application. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0066] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0067] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0068] Gaussian white noise is a noise model with uniform frequency distribution within the bandwidth and amplitude following Gaussian distribution. It has the simplicity of mathematical analysis and wide practical application value. It is an indispensable tool in communication, signal processing and control systems. Gaussian white noise generator can be realized by using physical noise source. Its original noise signal does not strictly conform to Gaussian distribution, and usually requires subsequent filtering and amplification processing to approximate white noise. Gaussian white noise generator implemented by digital hardware provides good distribution characteristics, with adjustable noise bandwidth and amplitude, which relies on random numbers as key resources. Compared with the traditional pseudo-random number generator widely used in Gaussian white noise generator, quantum random number generator is based on the basic principles of quantum physics. The quantum random numbers it generates are unpredictable and non-repeatable, with a clear source of randomness, and the minimum entropy theory can be used to strictly prove its randomness, so it has higher security. From the perspective of practicality, for application scenarios with high security requirements for Gaussian white noise, such as electronic countermeasures, Gaussian white noise generator needs to have the characteristics of unpredictability, adjustable noise signal amplitude, bandwidth, crest factor, and strong working stability.

[0069] In the process of realizing the concept of the present invention, it is found that: a Gaussian white noise generator can be realized by generating random number resources based on a quantum entropy source. However, unlike the traditional method of generating Gaussian white noise based on pseudo-random numbers, the Gaussian white noise generated based on the quantum entropy source needs to realize the amplification of the quantum noise signal and the real-time monitoring of the chip working state. At the same time, the original data needs to be subjected to random quantization extraction and post-processing, and it is necessary to ensure that the quantum random number resources are fully utilized to ensure that the Gaussian distribution random number rate generated by the transformation is used to generate the Gaussian white noise signal through the digital-to-analog conversion and amplification circuit. Therefore, how to realize a Gaussian white noise generator with adjustable amplitude, crest factor, and bandwidth based on the random numbers provided by the quantum entropy source as data resources, there is currently a lack of effective solutions.

[0070] In view of this, an embodiment of the present application provides a Gaussian white noise generator and a Gaussian white noise generation method, wherein the Gaussian white noise generator includes a quantum entropy source device, which is used to generate an initial quantum random number of a digital signal type based on the quantum entropy source; a bit width inversion device, including: a random extraction unit, which is used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number; a bit width adjustment unit, which is used to select a target queue corresponding to the target bit width based on a set target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with a target bit width, wherein different target queues correspond to different target bit widths; an inversion mapping unit, which is used to invert the second quantum random number based on a preset variable inversion mapping algorithm The method comprises the following steps: mapping processing to obtain an intermediate quantum random number with a Gaussian distribution, wherein the preset variable inverse mapping algorithm is an inverse cumulative distribution function constructed by a target geometric sequence determined according to a target bit width of the second quantum random number; a signal conversion device is used to convert the format of the intermediate quantum random number to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on the target bit width; wherein, when a target crest factor is input, a set target bit width is determined according to the target crest factor, so that the bit width adjustment unit selects a target queue corresponding to the target bit width for processing according to the target bit width, thereby generating a Gaussian white noise signal with a target crest factor.

[0071] In the embodiments of this application, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0072] Figure 1 A structural block diagram of a Gaussian white noise generator according to an embodiment of the present application is shown.

[0073] like Figure 1 As shown, the Gaussian white noise generator 100 comprises:

[0074] A quantum entropy source device 110, used to generate an initial quantum random number of a digital signal type based on a quantum entropy source 111;

[0075] The bit width inversion device 120 comprises:

[0076] A random extraction unit 121 is used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number;

[0077] A bit width adjustment unit 122 is used to select a target queue corresponding to the target bit width based on the set target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths;

[0078] An inversion mapping unit 123 is used to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number with a Gaussian distribution, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number;

[0079] A signal conversion device 130 is used to perform format conversion on the intermediate quantum random number to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on a target bit width;

[0080] When a target crest factor is input, a target bit width is set according to the target crest factor, so that the bit width adjustment unit 122 selects a target queue corresponding to the target bit width for processing according to the target bit width, thereby generating a Gaussian white noise signal with a target crest factor.

[0081] According to an embodiment of the present application, the quantum entropy source 111 is a device that generates random numbers using quantum physical phenomena. Unlike traditional pseudo-random number generators, the random numbers generated by the quantum random number generator (QRNG) are truly random numbers that cannot be predicted by any known algorithm or calculation. The quantum entropy source 111 generates random numbers through a single-photon path selection scheme, a single-photon arrival time measurement scheme, a laser phase fluctuation measurement scheme, a vacuum state fluctuation scheme, etc., that is, the initial quantum noise signal described below.

[0082] According to an embodiment of the present application, the bit width adjustment unit 122 realizes the conversion of random numbers of different target bit widths through different first-in-first-out queues (FIFO), so that when the first quantum random number is input, a second quantum random number with a Gaussian distribution of different precisions can be output. The above-mentioned first-in-first-out queue is the target queue, and different first-in-first-out queues are suitable for generating second quantum random numbers of different target bit widths.

[0083] According to an embodiment of the present application, the quantum entropy source 111 acts as a random source to provide an initial quantum noise signal, and the quantum entropy source device 110 converts it into an initial quantum random number of a digital signal type. Thereafter, the random extraction unit 121 in the bit width inversion device 120 is used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number. At the same time, when the user inputs the desired target crest factor, the set target bit width can be inferred based on formula (1).

[0084] (1)

[0085] in, represents the inverse error function, CF is the target crest factor, and w is the target bit width.

[0086] According to an embodiment of the present application, the bit width adjustment unit 122 selects a target queue corresponding to the target bit width based on the set target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, and then uses the inversion mapping unit 123 to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm, thereby obtaining an intermediate quantum random number with a Gaussian distribution, and finally uses the signal conversion device 130 to convert the intermediate quantum random number into a target quantum random number signal of an analog signal type, thereby obtaining a Gaussian white noise signal that obeys the Gaussian distribution and can be used for experiments.

[0087] According to an embodiment of the present application, an initial quantum random number of a digital signal type is generated by a quantum entropy source device 110 including a quantum entropy source 111, and a random extraction unit 121 is used to perform random extraction on the initial quantum random number to obtain a uniformly distributed first quantum random number, and then the target bit width is inferred based on the required target crest factor, and the bit width adjustment unit 122 is used to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with a target bit width, and then the inversion mapping unit 123 performs inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number with a Gaussian distribution, and finally the signal conversion device 130 is used to perform format conversion to obtain a Gaussian white noise signal of an analog signal type that obeys the Gaussian distribution. The quantum entropy source 111 generates true random numbers through the random characteristics of the quantum physics system itself. The randomness of the output is guaranteed by the basic principles of quantum mechanics and has a clear source. Therefore, the Gaussian white noise signal obtained in the above manner effectively improves the security and unpredictability of the Gaussian white noise generator 100, and further improves the integration and stability of the system integrated with the Gaussian white noise generator 100. At the same time, the target bit width is inferred by the target crest factor, and the target queue is selected for processing by the bit width adjustment unit 122, so that a Gaussian white noise signal with a target crest factor can be obtained, thereby improving the generation accuracy of the Gaussian white noise signal. At the same time, the Gaussian white noise generator of the embodiment of the present application can generate Gaussian white noise with an adjustable crest factor, thereby having the advantage of being suitable for the use requirements of a variety of different application scenarios.

[0088] Figure 2 A data processing flow chart of the Gaussian white noise generator 100 according to an embodiment of the present application is shown.

[0089] According to the embodiments of the present application, Figure 2 As shown, the quantum entropy source device 110 includes:

[0090] A quantum entropy source 111, used to generate an initial quantum noise signal;

[0091] The analog-to-digital converter 113 is used to convert the initial quantum noise signal into an initial quantum random number.

[0092] According to the embodiments of the present application, the quantum entropy source 111 can generate true random numbers based on the principles of quantum physics, with characteristics such as unpredictability, non-repeatability and unbiasedness, generate quantum random noise signals, and is a key component in the Gaussian white noise generator 100.

[0093] According to an embodiment of the present application, after the quantum entropy source 111 generates an initial quantum noise signal of an analog signal type, the initial quantum noise signal needs to be converted into a digital signal type before bit width adjustment and inversion mapping processing can be performed. Therefore, the initial quantum noise signal of the analog signal type is converted into an initial quantum random number of a digital signal type through the analog-to-digital converter 113.

[0094] According to an embodiment of the present application, the quantum entropy source device 110 further includes:

[0095] The first signal amplifier 112 is used to perform signal amplification processing on the initial quantum noise signal to obtain an amplified initial quantum noise signal.

[0096] According to an embodiment of the present application, the initial quantum noise signal generated by the quantum entropy source 111 requires an adjustable first signal amplifier 112 to be adjusted in order to meet the measurement range of the subsequent analog-to-digital converter 113. The first signal amplifier 112 must meet the high gain and high bandwidth requirements to ensure that the requirements of the minimum entropy theory can be met in the subsequent randomness quantization process.

[0097] According to an embodiment of the present application, the randomness of the random extraction unit 121 is quantified by minimum entropy, which is defined as shown in formula (2):

[0098] (2)

[0099] in, is the probability of the most likely outcome, is the minimum entropy value.

[0100] According to the embodiment of the present application, since the randomness of the random extraction unit 121 is derived from the basic principles of quantum physics and is provable by information theory, the first quantum random number obtained is usable, high-speed and stable.

[0101] According to an embodiment of the present application, the intermediate quantum random number of the Gaussian distribution is obtained by a preset variable inversion mapping algorithm of the inversion mapping unit 123 according to the uniformly distributed second quantum random number. Among them, different preset parameters correspond to different input second quantum random number bit widths. The larger the bit width adjusted by the bit width adjustment unit 122, the higher the accuracy of the original uniformly distributed data. When the target bit width of the input first quantum random number is w, its accuracy is equivalent to The range of the second quantum random number with Gaussian distribution thus outputted is also larger. Thus, the inversion mapping unit 123 can output intermediate quantum random numbers with Gaussian distribution.

[0102] According to the embodiments of the present application, the Gaussian white noise signal generated by the Gaussian white noise generator 100 has good unpredictability and non-repeatability, its randomness source is clear, and its randomness can be strictly proved by the minimum entropy theory. Therefore, the Gaussian white noise generator 100 of the present application has higher security.

[0103] According to an embodiment of the present application, randomness extraction is performed on the initial quantum random number based on the minimum entropy theory to obtain a uniformly distributed first quantum random number, including:

[0104] Determine the minimum entropy value based on the minimum entropy theory;

[0105] The initial quantum random number is transformed based on the target matrix to obtain an intermediate quantum random number;

[0106] Determine the quantum random ratio according to the intermediate quantum random number and the initial quantum random number;

[0107] When the quantum random ratio is less than the minimum entropy value, the intermediate quantum random number is determined as the first quantum random number.

[0108] According to the embodiment of the present application, the minimum entropy value can be obtained based on formula (2): , where the minimum entropy value It is obtained by measuring the initial quantum random number by the random extraction unit 121, so that the minimum entropy value The estimate is more accurate.

[0109] According to an embodiment of the present application, a Toeplitz matrix algorithm based on fast Fourier transform is used to obtain a target matrix, the size of which is n×m. The intermediate quantum random numbers containing m random numbers are extracted from the n random numbers of the initial quantum random number clock through the target matrix.

[0110] According to an embodiment of the present application, if the quantum random ratio n / m < , at this time, the intermediate quantum random number containing m random numbers can be determined as the first quantum random number.

[0111] Figure 3 A schematic diagram of a curve of an intermediate standard Gaussian distribution function according to an embodiment of the present application is shown. Figure 4 A schematic diagram of a curve of a target standard Gaussian distribution function according to an embodiment of the present application is shown. Figure 5 A segmented schematic diagram of a symmetric curve according to an embodiment of the present application is shown.

[0112] According to an embodiment of the present application, performing inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number includes:

[0113] generating an intermediate standard Gaussian distribution function according to the second quantum random number, the initial standard Gaussian distribution function and the target variable;

[0114] Based on the target variable, the intermediate standard Gaussian distribution function is inverted to obtain a target standard Gaussian distribution function and an inverse cumulative distribution curve corresponding to the target standard Gaussian distribution function, wherein the inverse cumulative distribution curve includes two symmetrical curves of symmetrical distribution;

[0115] For any symmetric curve, the target geometric sequence is used to segment and equally divide the symmetric curve to obtain multiple initial segmented curve functions;

[0116] An intermediate quantum random number is generated according to the second quantum random number and a plurality of initial piecewise curve functions.

[0117] According to an embodiment of the present application, the inversion method is a widely used hardware method that converts uniform random numbers into Gaussian random numbers through an inverse cumulative distribution function (ICDF). The preset variable inversion mapping algorithm is a versatile technique that uses ICDF in combination with uniform random variables to generate various probability distributions, including standard Gaussian distribution.

[0118] According to the embodiment of the present application, based on the initial standard Gaussian distribution function and the target variable t, an intermediate standard Gaussian distribution function as shown in formula (3) is generated. The function curve is shown in Figure 3 GCDF (Gaussian Cumulative Distribution Function) curve.

[0119] (3)

[0120] Among them, formula (3) is the mathematical expression of the standard Gaussian distribution function. When the independent variable is the target variable t, is the GCDF value, and m is the parameter to be substituted.

[0121] It should be noted that Figure 3 It is the function curve of the intermediate standard Gaussian distribution function in the rectangular coordinate system. The range of the horizontal axis independent variable is (-∞, +∞), and the vertical axis is the function value corresponding to the independent variable in the mathematical expression of the GCDF function. The range of values ​​is [0,1]. Both the horizontal and vertical axes are numerical values ​​without units.

[0122] According to an embodiment of the present application, the intermediate standard Gaussian distribution function is inverted based on the target variable t to obtain a target standard Gaussian distribution function as shown in formula (4). The target standard Gaussian distribution function is the inverse cumulative distribution function of formula (3). The curve of the inverse cumulative distribution function is shown in Figure 4 GCDF -1 curve:

[0123] (4)

[0124] in, represents the inverse error function. Substituting the second quantum random number x as the independent variable m into formula (4) for calculation, we can obtain (Right now ), after this transformation, we get the intermediate quantum random number with Gaussian distribution that satisfies formula (3).

[0125] It should be noted that Figure 4 is the inverse cumulative distribution function GCDF -1 The function curve in the rectangular coordinate system, the horizontal axis independent variable range is [0,1], the vertical axis is the independent variable in GCDF -1 The corresponding function value in the function mathematical expression has a value range of (-∞, +∞). The horizontal and vertical coordinates are both numerical values ​​and have no units.

[0126] According to the embodiment of the present application, the inverse cumulative distribution curve is an odd function centered at 0.5, so only half of the values ​​need to be fitted and calculated. The other half can be obtained through symmetry, that is, the inverse cumulative distribution curve includes two symmetrical curves of symmetrical distribution.

[0127] According to the embodiment of the present application, for a symmetrical curve with a horizontal coordinate between 0 and 0.5, since the curve changes more drastically when the value of the independent variable is closer to 0, the segmentation method should not adopt a uniform segmentation method, but a geometric progression method for segmentation, and the original independent variable is segmented according to a geometric progression of powers of 2, and the segmentation is as follows: Figure 5 shown.

[0128] It should be noted that Figure 5 is the inverse cumulative distribution function GCDF -1 In the rectangular coordinate system, the horizontal axis is the function curve of the independent variable in the range of [0,0.5], and the vertical axis is the independent variable in GCDF -1 The function value corresponding to the function mathematical expression has a value range of (-∞, 0], and the horizontal and vertical axes are both numerical values ​​without units.

[0129] Specifically, for different target bit widths w, when the value range of the w-bit data is [0,2 w-1], the data interval is 1, and when it is input as (0,1) distributed data, the data interval is , that is, its accuracy is Therefore, the fitting segment nodes for the inverse cumulative distribution curve are , , Until There are w-1 intervals in total. According to the actual test results, each segment is divided into 4 equal parts (or other numbers of equal parts) for fitting to obtain multiple initial segment curve functions, thereby generating intermediate quantum random numbers based on the second quantum random number and multiple initial segment curve functions.

[0130] According to an embodiment of the present application, in a bit width inversion device 120 based on a programmable logic device, the core task is to efficiently calculate the ICDF, where the inherent parallelism of the programmable logic device and its advantages in fixed-point operations provide significant advantages. The preset variable inversion mapping algorithm utilizes adders, multipliers, and lookup tables, in which polynomial coefficients in the programmable logic device are stored. The preset variable inversion mapping algorithm generates a fast and stable Gaussian distributed random number stream, which is aligned with the dedicated bit width of the digital-to-analog converter 131, thereby optimizing the utilization of limited quantum random number resources. In order to address the computational requirements, a lookup table can be used to store polynomial parameters and a piecewise fitting technique.

[0131] Figure 6 A schematic diagram of generating a Gaussian white noise signal according to an embodiment of the present application is shown.

[0132] According to an embodiment of the present application, each initial piecewise curve function has a plurality of initial polynomial coefficients.

[0133] According to the embodiments of the present application, Figure 6 As shown, according to the second quantum random number and a plurality of initial piecewise curve functions, an intermediate quantum random number is generated, including:

[0134] Processing the second quantum random number to obtain a sign bit, an address bit, and a calculation bit, wherein the sign bit, the address bit, and the calculation bit all have corresponding bit widths;

[0135] Based on the bit width of the address bits, determining a plurality of target polynomial coefficients in a target piecewise curve function corresponding to the target bit width;

[0136] An intermediate quantum random number is generated according to a plurality of target polynomial coefficients, a calculation bit and a sign bit.

[0137] According to the embodiments of the present application, Figure 6As shown, the second quantum random number is divided into three parts: a sign bit, an address bit, and a calculation bit, wherein the sign bit is the first bit of the second quantum random number, the address bit determines the quadratic polynomial coefficients that the calculation bit needs to correspond to, and according to the value of the address bit width, a target piecewise curve function corresponding to the target bit width and a plurality of target polynomial coefficients of the target piecewise curve function are determined from a plurality of initial piecewise curve functions, so that the target polynomial coefficients are input into the quadratic polynomial fitting calculation part, and the final calculation result is combined with the sign bit of the first bit to obtain the required Gaussian distribution random number, i.e., the intermediate quantum random number. After obtaining the intermediate quantum random number, it can also be truncated to meet the output bit width of the digital-to-analog converter 131.

[0138] In the above process, the address bit width is W addr , calculate the bit width W in , the target polynomial coefficient is W c0 , W c1 , W c2 , the final output unsigned bit width is W q , plus the sign bit width is W out , the final output bit width required by the digital-to-analog converter 131 is W DAC .

[0139] According to the different quantum random number widths w, it is necessary to update the parameter values ​​in the quadratic polynomial fitting lookup table according to the fitting polynomial parameters in the memory unit, which are the fitting polynomial coefficients of the inverse function of the cumulative inverse function of the Gaussian function corresponding to the quantum random number width w. This coefficient needs to satisfy the value range of the w-bit data [0,2 w -1], the output truncated data can completely fall within the output bit width w required by the final digital-to-analog converter 131. DAC The value range of -1], that is, the minimum value needs to be 0, and the maximum value after rounding is just less than -1. That is, after each external triggering of the target bit width update instruction, the initial polynomial coefficients need to be updated. This step is to ensure that the output Gaussian distribution random number can be close to the full scale of the digital-to-analog converter 131 under different input bit widths, thereby improving the dynamic range and accuracy of the output Gaussian white noise.

[0140] It should be noted that in order to ensure the accuracy of the Gaussian white noise signal, the target input bit width w must not be much lower than W. DAC , the minimum requirement is to be compatible with W DAC Close, the calculation bit width is W in To ensure accuracy, the size and the unsigned bit width of the final output must be W. q Close to, the target polynomial coefficient W c0 The bit width must be greater than WDAC , more than twice, W c0 , W c1 The full scale requirement of the digital-to-analog converter 131 is determined by W in and W c0 The value of is determined.

[0141] Figure 7 A schematic diagram of generating intermediate quantum random numbers according to an embodiment of the present application is shown.

[0142] According to an embodiment of the present application, the plurality of target polynomial coefficients include a first coefficient (ie Figure 7 Coe0 and Figure 6 W c0 ), the second coefficient (i.e. Figure 7 Coe1 and Figure 6 W c1 ) and the third coefficient (i.e. Figure 7 Coe2 and Figure 6 W c2 ).

[0143] According to the embodiments of the present application, reference Figure 7 , according to multiple target polynomial coefficients, calculation bits and sign bits, generate intermediate quantum random numbers, including:

[0144] Processing the third coefficient and the calculation bit using the first multiplier to generate a first intermediate bit;

[0145] Processing the second coefficient and the first intermediate bit using the first adder to generate a second intermediate bit;

[0146] Processing the calculated bit and the second intermediate bit using a second multiplier to generate a third intermediate bit;

[0147] The third intermediate bit, the first coefficient and the sign bit are processed by the second adder to generate an intermediate quantum random number.

[0148] According to the embodiments of the present application, referring to Figure 7 The second quantum random number can be divided into a sign bit, an address bit and a calculation bit through a leading zero detector and a shift register. The address bit is determined to which initial piecewise curve function it belongs based on its bit width, so that the initial polynomial coefficients of the initial piecewise curve function are determined as the target polynomial coefficients Coe0, Coe1 and Coe2, respectively. Figure 7 The intermediate quantum random number can be obtained by performing operations with the adder and multiplier shown.

[0149] According to an embodiment of the present application, the specific process starts with a leading zero detector that identifies the position of the most significant zero bit in the second quantum random number. This position determines the segment number, corresponding to the number of leading zeros. In addition, the two bits that follow are extracted to select an address in one of the four segment areas. The segment number and the quantum random number are then passed to a shift register, where the data is left-shifted. This step reduces the bit width of the data, thereby optimizing computational efficiency. The truncated quantum random value is fitted with multiple target polynomial coefficients. The quadratic polynomial segmented fitting method strikes a balance between computational efficiency and accuracy, ensuring the statistical validity of the output random number (i.e., the intermediate quantum random number).

[0150] According to an embodiment of the present application, the bandwidth and crest factor of Gaussian white noise are completely dependent on the data bit width of the quantum random number output by the data bit width integer module when the original quantum random number adopts the ICDF function of the standard Gaussian distribution. Selecting a quantum random number with a higher bit width requires reducing the clock frequency of the Gaussian distribution random number calculation, which in turn directly affects the bandwidth of the generated Gaussian white noise signal. At the same time, increasing the amount of input data will increase the mapping range of the generated Gaussian random number, thereby increasing the crest factor of the Gaussian white noise. The crest factor reflects the width of the distribution tail, which is defined as the ratio of the peak value of the noise signal to its root mean square value (RMS), as shown in formula (5) where Vpp represents the peak-to-peak value and CF is the crest factor.

[0151] (5)

[0152] Where Vpp=2 , is the root mean square value.

[0153] According to an embodiment of the present application, in Gaussian white noise, the root mean square value (RMS) is equal to the standard deviation σ, because the mean μ=0. Therefore, the maximum value of the Gaussian random number is the value of CF, thereby obtaining the corresponding function of the crest factor and the target bit width as shown in formula (1). The bandwidth of Gaussian white noise is negatively correlated with its one-to-one corresponding crest factor value.

[0154] According to an embodiment of the present invention, different polynomial parameters corresponding to the preset variable inversion mapping algorithm are preset and stored in a memory unit, and different target polynomial coefficients can be used for inversion mapping processing according to different target bit widths, thereby realizing the function of adjustable bandwidth and crest factor, wherein the bandwidth and the bit width are in a one-to-one correspondence.

[0155] According to an embodiment of the present application, the signal conversion device 130 includes:

[0156] The digital-to-analog converter 131 is used to convert the intermediate quantum random number into a target quantum random number signal.

[0157] According to an embodiment of the present application, the signal conversion device 130 generates a Gaussian white noise signal of an analog signal type through the digital-to-analog converter 131 from the intermediate quantum random number.

[0158] According to an embodiment of the present application, the signal conversion device 130 further includes:

[0159] The second signal amplifier 132 is used to perform signal amplification processing on the target quantum random number signal to obtain an amplified target quantum random number signal.

[0160] According to an embodiment of the present application, the second signal amplifier 132 can amplify the target quantum random number signal to implement an amplifier gain adjustment function.

[0161] According to an embodiment of the present application, the Gaussian white noise generator 100 further includes:

[0162] The state monitoring device 140 is used to monitor the working state of the quantum entropy source 111 to adjust the working states of the quantum entropy source 111 and the first signal amplifier 112 according to the monitoring result.

[0163] According to an embodiment of the present application, the state monitoring device 140 includes a peripheral circuit and a monitoring unit. The peripheral circuit can collect signals about the working state of the quantum entropy source 111 and transmit them to the monitoring unit. The monitoring unit can adjust the working states of the quantum entropy source 111, the first signal amplifier 112 and the second signal amplifier 132 according to the analysis results of the minimum entropy quantization, for example, the gain amplitude of the signal amplifier can be adjusted.

[0164] According to the embodiments of the present application, any one or more of the devices, modules, and units, or at least part of the functions of any one of them, can be implemented in one module. According to the embodiments of the present application, any one or more of the devices, modules, and units can be split into multiple modules for implementation. According to the embodiments of the present application, any one or more of the devices, modules, and units can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application-specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present application, one or more of the devices, modules, and units can be at least partially implemented as computer program modules, and when the computer program module is run, the corresponding functions can be executed.

[0165] Figure 8 A flowchart of a method for generating Gaussian white noise according to an embodiment of the present application is shown.

[0166] like Figure 8 As shown, the Gaussian white noise generation method includes operations S801 to S806:

[0167] In operation S801, based on a quantum entropy source, an initial quantum random number of a digital signal type is generated using a quantum entropy source device;

[0168] In operation S802, a random extraction unit is used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number;

[0169] In operation S803, when a target crest factor is input, a target bit width is determined according to the target crest factor;

[0170] In operation S804, based on the set target bit width, a bit width adjustment unit is used to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths;

[0171] In operation S805, an inversion mapping unit is used to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number, wherein the bit width inversion device includes a random extraction unit, a bit width adjustment unit and an inversion mapping unit;

[0172] In operation S806, the intermediate quantum random number is format-converted by using a signal conversion device to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on a target bit width.

[0173] According to the embodiment of the present application, the quantum entropy source is used as a random source to provide an initial quantum noise signal, and the quantum entropy source device converts it into an initial quantum random number of a digital signal type. Thereafter, the random extraction unit in the bit width inversion device is used to extract the initial quantum random number randomly, and obtain a uniformly distributed first quantum random number. At the same time, when the user inputs the desired target crest factor, the set target bit width can be inferred based on formula (1). The bit width adjustment unit selects a target queue corresponding to the target bit width based on the set target bit width to adjust the bit width of the first quantum random number, and obtains a second quantum random number with the target bit width. Then, the inversion mapping unit is used to perform inversion mapping on the second quantum random number based on a preset variable inversion mapping algorithm, thereby obtaining an intermediate quantum random number with a Gaussian distribution. Finally, the signal conversion device is used to convert the intermediate quantum random number into a target quantum random number signal of an analog signal type, thereby obtaining a Gaussian white noise signal that obeys the Gaussian distribution and can be used for experiments.

[0174] According to an embodiment of the present application, an initial quantum random number of a digital signal type is generated by a quantum entropy source device including a quantum entropy source, and a random extraction unit is used to perform random extraction on the initial quantum random number to obtain a uniformly distributed first quantum random number, and then the target bit width is inferred based on the required target crest factor, and a bit width adjustment unit is used to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with a target bit width, and then an inversion mapping unit performs inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number with a Gaussian distribution, and finally a Gaussian white noise signal of an analog signal type that obeys a Gaussian distribution is obtained by performing format conversion through a signal conversion device. The quantum entropy source generates true random numbers through the random characteristics of the quantum physics system itself. The randomness of the output is guaranteed by the basic principles of quantum mechanics and has a clear source. Therefore, the Gaussian white noise signal obtained in the above manner effectively improves the security and unpredictability of the Gaussian white noise generator, and further improves the integration and stability of the system integrated with the Gaussian white noise generator. At the same time, the target bit width is inferred by the target crest factor, and the target queue is selected for processing by the bit width adjustment unit, so that a Gaussian white noise signal with a target crest factor can be obtained, thereby improving the generation accuracy of the Gaussian white noise signal. At the same time, the Gaussian white noise generator of the embodiment of the present application can generate Gaussian white noise with an adjustable crest factor, thereby having the advantage of being suitable for the use requirements of a variety of different application scenarios.

[0175] It should be noted that the Gaussian white noise generation method part in the embodiment of the present application corresponds to the Gaussian white noise generator part in the embodiment of the present application. The description of the Gaussian white noise generation method part specifically refers to the Gaussian white noise generator part, which will not be repeated here.

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It will be appreciated by those skilled in the art that the features recorded in the various embodiments of the present application can be combined and / or combined in a variety of ways, even if such a combination or combination is not clearly recorded in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application.

[0177] The embodiments of the present application are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present application. Although each embodiment is described above, this does not mean that the measures in each embodiment cannot be used in combination advantageously. The present application does not depart from the scope of the present application, and those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present application.

Claims

1. A Gaussian white noise generator, characterized in that: include: A quantum entropy source device, used for generating an initial quantum random number of a digital signal type based on the quantum entropy source; The bit width inversion device comprises: A random extraction unit, used to extract randomness from the initial quantum random number to obtain a uniformly distributed first quantum random number; A bit width adjustment unit is used to select a target queue corresponding to the target bit width based on a set target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths; an inversion mapping unit, configured to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number with a Gaussian distribution, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number; A signal conversion device, used for performing format conversion on the intermediate quantum random number to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on the target bit width; Among them, when the target crest factor is input, the set target bit width is determined according to the target crest factor, so that the bit width adjustment unit selects the target queue corresponding to the target bit width for processing according to the target bit width, thereby generating a Gaussian white noise signal with the target crest factor.

2. The Gaussian white noise generator according to claim 1, characterized in that: The quantum entropy source device comprises: The quantum entropy source is used to generate an initial quantum noise signal; An analog-to-digital converter is used to convert the initial quantum noise signal into the initial quantum random number.

3. The Gaussian white noise generator according to claim 2, characterized in that: The quantum entropy source device also includes: The first signal amplifier is used to perform signal amplification processing on the initial quantum noise signal to obtain an amplified initial quantum noise signal.

4. The Gaussian white noise generator according to claim 1, characterized in that: The second quantum random number is subjected to inversion mapping processing based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, including: Generate an intermediate standard Gaussian distribution function according to the second quantum random number, the initial standard Gaussian distribution function and the target variable; Based on the target variable, the intermediate standard Gaussian distribution function is inverted to obtain a target standard Gaussian distribution function and an inverse cumulative distribution curve corresponding to the target standard Gaussian distribution function, wherein the inverse cumulative distribution curve includes two symmetrical curves of symmetrical distribution; For any of the symmetric curves, segment and equally divide the symmetric curve using the target geometric sequence to obtain a plurality of initial segmented curve functions; The intermediate quantum random number is generated according to the second quantum random number and a plurality of the initial piecewise curve functions.

5. The Gaussian white noise generator according to claim 4, characterized in that: Each of the initial piecewise curve functions has a plurality of initial polynomial coefficients; The step of generating the intermediate quantum random number according to the second quantum random number and the plurality of initial piecewise curve functions comprises: Processing the second quantum random number to obtain a sign bit, an address bit, and a calculation bit, wherein the sign bit, the address bit, and the calculation bit all have corresponding bit widths; Based on the bit width of the address bits, determining a plurality of target polynomial coefficients in a target piecewise curve function corresponding to the target bit width; The intermediate quantum random number is generated according to a plurality of the target polynomial coefficients, the calculation bit and the sign bit.

6. The Gaussian white noise generator according to claim 5, characterized in that: The plurality of target polynomial coefficients include a first coefficient, a second coefficient, and a third coefficient; The step of generating the intermediate quantum random number according to the plurality of target polynomial coefficients, the calculation bit and the sign bit comprises: Processing the third coefficient and the calculated bit using a first multiplier to generate a first intermediate bit; Processing the second coefficient and the first intermediate bit using a first adder to generate a second intermediate bit; Processing the calculated bit and the second intermediate bit using a second multiplier to generate a third intermediate bit; The third intermediate bit, the first coefficient and the sign bit are processed by a second adder to generate the intermediate quantum random number.

7. The Gaussian white noise generator according to claim 1 or 4, characterized in that: The initial quantum random number is randomly extracted to obtain a uniformly distributed first quantum random number, including: Determine the minimum entropy value based on the minimum entropy theory; Converting the initial quantum random number based on a target matrix to obtain an intermediate quantum random number; Determining a quantum random ratio according to the intermediate quantum random number and the initial quantum random number; When the quantum random ratio is less than the minimum entropy value, the intermediate quantum random number is determined as the first quantum random number.

8. The Gaussian white noise generator according to claim 1, characterized in that: The signal conversion device comprises: A digital-to-analog converter, used to convert the intermediate quantum random number into the target quantum random number signal; Wherein, the signal conversion device further includes: The second signal amplifier is used to perform signal amplification processing on the target quantum random number signal to obtain an amplified target quantum random number signal.

9. The Gaussian white noise generator according to claim 3, characterized in that: Also includes: A state monitoring device is used to monitor the working state of the quantum entropy source to adjust the working states of the quantum entropy source and the first signal amplifier according to the monitoring result.

10. A method for generating Gaussian white noise, characterized in that: include: Based on a quantum entropy source, an initial quantum random number of a digital signal type is generated using a quantum entropy source device; The random extraction unit is used to extract the initial quantum random number randomly to obtain a uniformly distributed first quantum random number. When a target crest factor is input, a target bit width is determined according to the target crest factor; Based on the set target bit width, using a bit width adjustment unit to select a target queue corresponding to the target bit width to perform bit width adjustment processing on the first quantum random number to obtain a second quantum random number with the target bit width, wherein different target queues correspond to different target bit widths; Using an inversion mapping unit to perform inversion mapping processing on the second quantum random number based on a preset variable inversion mapping algorithm to obtain an intermediate quantum random number, wherein the preset variable inversion mapping algorithm is an inverse cumulative distribution function constructed according to a target geometric sequence determined according to a target bit width of the second quantum random number, wherein the bit width inversion device includes the random extraction unit, the bit width adjustment unit and the inversion mapping unit; The intermediate quantum random number is format-converted by using a signal conversion device to obtain a target quantum random number signal of an analog signal type, wherein the target quantum random number signal represents a Gaussian white noise signal, and the Gaussian white noise signal has a crest factor determined based on the target bit width.

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