CN117151237A - Quantum random number generation method and device based on diode electron tunneling effect - Google Patents
Quantum random number generation method and device based on diode electron tunneling effect Download PDFInfo
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Abstract
The disclosure provides a quantum random number generation method based on diode electron tunneling effect, comprising the following steps: acquiring a current time domain signal passing through a diode with an electron tunneling effect; the noise component of the current time domain signal includes non-quantum noise and quantum noise; analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component; based on the spectrum amplitude of a target spectrum area in the spectrogram, calculating to obtain Gaussian distribution variance of quantum noise in a time domain; and obtaining a first random number sequence according to the correlation of the current value sampled for multiple times and the probability density map of the current time domain signal, and extracting a quantum random number from the first random number sequence based on the Gaussian distribution variance of quantum noise in the time domain. Therefore, the quantum random number can be simply and conveniently generated by utilizing the semiconductor and the electronic device.
Description
Technical Field
The application relates to the technical field of quantum communication, in particular to a quantum random number generation method and device based on diode electron tunneling effect.
Background
Random numbers have important applications in the fields of modern cryptography, simulation experiments, information processing, and the like. Conventional computer systems employ pseudo-random number generators to generate random numbers, but the result is actually generated by deterministic algorithms, which do not provide true randomness. This pseudo-randomness may present a safety hazard in certain scenarios. To solve this problem, quantum random number generation schemes have been developed. The uncertainty principle of quantum mechanics provides a basis for achieving true randomness. Quantum random number generation exploits the random properties of quantum physical systems, such as the unpredictability of single photons, the randomness of quantum measurements, the randomness of quantum tunneling effects, etc., to produce truly random arrays of numbers.
Most quantum random number schemes are currently based on optical platforms, for example, using information such as the arrival time, phase, etc. of photons to generate random numbers. A common feature of these schemes is the need to detect photons, convert the optical signals into electrical signals for processing, often requiring complex optics and experimental setup, involving calibration and adjustment of the optics, which are relatively complex to implement and relatively costly.
Therefore, there is a need to propose a simpler and more convenient method of generating quantum random numbers.
Disclosure of Invention
In order to solve the problems, the application provides a quantum random number generation method, a device and electronic equipment based on diode electron tunneling effect, which can simply and conveniently generate quantum random numbers by utilizing common semiconductors and circuit devices.
In a first aspect, the present application provides a method for generating a quantum random number based on diode electron tunneling effect, the method comprising: acquiring a current time domain signal passing through a diode with an electron tunneling effect; the noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by the electron tunneling effect of the diode; analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component; based on the spectrum amplitude of a target spectrum area in the spectrogram, calculating to obtain Gaussian distribution variance of quantum noise in a time domain; spectral features of the target spectral region are only correlated with non-quantum noise; and obtaining a first random number sequence according to the correlation of the current value sampled for multiple times and the probability density map of the current time domain signal, and extracting a quantum random number from the first random number sequence based on the Gaussian distribution variance of quantum noise in the time domain.
Therefore, the Gaussian distribution variance of the quantum noise in the time domain can be obtained by acquiring the current time domain signal passing through the diode with the electron tunneling effect and analyzing the frequency spectrum of the current time domain signal. And obtaining a first random number sequence according to a probability density diagram of the current time domain signal, and extracting quantum random numbers from the first random number sequence based on Gaussian distribution variance of quantum noise in a time domain. The scheme does not relate to optical devices and optical systems, and can improve the convenience of generating quantum random numbers.
In one possible implementation, the diode is reverse biased by a dc regulated power supply, the dc regulated power supply and the diode being connected in series, the voltage applied across the diode being set according to a preset range, the preset range being related to the reverse breakdown voltage of the diode.
In one possible implementation manner, based on a spectrum amplitude of a target spectrum region in a spectrogram, a gaussian distribution variance of quantum noise in a time domain is calculated, including: estimating the signal intensity ratio of the quantum noise and the non-quantum noise in the noise component; obtaining the power spectral density of the non-quantum noise in the target spectral region based on the spectral amplitude of the target spectral region;
according to the signal intensity ratio and the power spectrum density of the non-quantum noise in the target spectrum region, the Gaussian distribution variance of the quantum noise in the time domain is calculated as follows:wherein gamma is the signal intensity ratio, S (0) is the power spectral density of non-quantum noise in the target spectral region, omega min 、ω max Is a preset frequency value contained in a specified spectral region.
In one possible implementation, obtaining the first random number sequence according to the correlation of the current value sampled multiple times and the probability density map of the current time domain signal includes: longitudinally dividing a current distribution area of the probability density map into a plurality of subareas; each of the plurality of sub-regions corresponds to a current value interval of a different range; setting random numbers with the same digits for each of a plurality of subareas respectively; wherein the random numbers in the different sub-regions are different; and obtaining a first random number sequence based on random numbers respectively corresponding to the subareas where the current values obtained by multiple current sampling experiments fall.
In one possible implementation manner, based on random numbers corresponding to sub-regions where a plurality of current values obtained through a plurality of current sampling experiments fall, a first random number sequence is obtained, including: obtaining a first current value passing through a diode for each current sampling experiment; determining a first subarea corresponding to a current value interval to which a first current value belongs in a probability density map; taking the random number corresponding to the first subarea as the random number obtained by the current sampling experiment; the first random number sequence is determined based on all random numbers obtained by multiple current sampling experiments.
In one possible implementation, extracting the quantum random number from the first random number sequence based on a gaussian distribution variance of the quantum noise in a time domain includes: obtaining a first minimum entropy of the first random number sequence through calculation; determining a probability density map of the quantum noise based on the Gaussian distribution variance of the quantum noise in a time domain; calculating a second minimum entropy of the quantum random number based on the probability density map of the quantum noise; a quantum random number is extracted from the first random number sequence based on the first minimum entropy and the second minimum entropy.
In one possible implementation, extracting the quantum random number from the first random number sequence based on the first minimum entropy and the second minimum entropy includes: determining a number of rows of the Toeplitz matrix using the first minimum entropy, and determining a number of columns of the Toeplitz matrix using the second minimum entropy; constructing a Toeplitz matrix based on the number of rows and the number of columns; a quantum random number is extracted from the first random number sequence based on the Toeplitz matrix.
In a second aspect, the present application provides a circuit for generating quantum noise, the circuit comprising a diode and a dc regulated power supply, the dc regulated power supply reverse stressing the diode, the dc regulated power supply and the diode being connected in series, the voltage applied across the diode being set according to a predetermined range, the predetermined range being related to the reverse breakdown voltage of the diode; the diode generates electron tunneling effect under the action of reverse voltage; the current time domain signal passing through the diode contains a noise component, wherein the noise component comprises non-quantum noise and quantum noise, and the quantum noise is generated by an electron tunneling effect; the current time domain signal is used for obtaining a spectrogram corresponding to the noise component; the spectrum amplitude of a target spectrum area in the spectrogram is used for obtaining Gaussian distribution variance of quantum noise in a time domain; spectral features of the target spectral region are only correlated with non-quantum noise; the probability density map of the current time domain signal is used for obtaining a first random number sequence, and the Gaussian distribution variance of the quantum noise in the time domain is used for extracting quantum random numbers from the first random number sequence.
In a third aspect, the present application provides a quantum random number generating device based on diode electron tunneling effect, the device comprising: the acquisition unit is used for acquiring a current time domain signal passing through the diode with the electron tunneling effect; the noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by the electron tunneling effect of the diode; the processing unit is used for analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component; the processing unit is also used for calculating and obtaining Gaussian distribution variance of the quantum noise in a time domain based on the frequency spectrum amplitude of the target frequency spectrum region in the frequency spectrum diagram; spectral features of the target spectral region are only correlated with non-quantum noise; the processing unit is further used for obtaining a first random number sequence according to the correlation of the current value sampled for a plurality of times and the probability density map of the current time domain signal, and extracting a quantum random number from the first random number sequence based on Gaussian distribution variance of quantum noise in the time domain.
In a fourth aspect, the present application provides an electronic device comprising: at least one memory for storing a program; at least one processor for executing programs stored in the memory; wherein the processor is adapted to perform the method described in the first aspect or any one of the possible implementations of the first aspect, when the memory-stored program is executed.
It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a circuit diagram of a quantum noise generation circuit provided by an embodiment of the present application;
FIG. 2 is a diagram of a current time domain signal according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for generating quantum random numbers based on diode electron tunneling effect according to an embodiment of the present application;
FIG. 4 is a graph of probability density of a current time domain signal according to an embodiment of the present application;
FIG. 5 is a graph of a noise spectrum corresponding to each of the time domain signals of different currents according to an embodiment of the present application;
FIG. 6a is a graph of a noise subcomponent spectrum of a current time domain signal according to an embodiment of the present application;
FIG. 6b is a non-quantum noise spectrum of a current time domain signal according to an embodiment of the present application;
FIG. 7 is a graph showing a probability density curve of a current time domain signal according to an embodiment of the present application;
fig. 8 is a diagram of a quantum random number generating device based on diode electron tunneling effect according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be described below with reference to the accompanying drawings.
In describing embodiments of the present application, words such as "exemplary," "such as" or "for example" are used to mean serving as examples, illustrations or explanations. Any embodiment or design described herein as "exemplary," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a alone, B alone, and both A and B. In addition, unless otherwise indicated, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Quantum random number generation (Quantum Random Number Generator, QRNG) is a scheme based on quantum mechanics principles to generate true random number sequences.
The current common quantum random number generation scheme based on an optical platform mainly comprises the following steps:
single photon counting scheme: this scheme uses a single photon source, such as a weak laser, to generate one photon stream. The photons are subjected to single photon counting by a photon counter, converting the photon stream into discrete electrical signals. Since the arrival time of photons is unpredictable, the time interval of each count is considered to be a truly random number.
Random phase scheme: the scheme utilizes a laser to generate a coherent light wave and generates random numbers by introducing random phase changes. The random phase may be introduced in a variety of ways, for example using an optical modulator, random disturbance of the optical fiber or disturbance of the external environment, etc. The unpredictability of the phase of the light waves causes the amplitude and phase of the light waves to become unpredictable, thereby generating truly random numbers.
Photon entanglement scheme: the scheme utilizes the characteristic of quantum entanglement, and realizes the generation of random numbers by measuring entangled photon pairs. Photon entanglement is a special quantum state in which the states between two or more photons are interrelated, and the measurement of one photon immediately affects the states of the other photons. Using the photon entanglement measurements, a truly random number sequence can be generated.
These quantum random number generation schemes based on optical platforms all use the quantum properties of photons and the control of the optics to generate a truly random number sequence, but these techniques inevitably have the following inherent disadvantages due to the use of optical systems:
high cost: quantum random number generation schemes based on optical platforms typically require complex optics and experimental setup, which makes them relatively costly. The manufacture, calibration and maintenance of equipment and devices requires expertise and skill, which increases the cost of deployment and use.
Technical complexity: the quantum random number generation scheme of the optical platform involves control and adjustment of the optical device, requiring precise experimental setup and parameter adjustment. This requires the operator to have a certain expertise and skill, increasing the complexity and technical threshold of use.
Low speed performance limitations: while photon-based quantum random number schemes can achieve high-speed quantum random number generation, current photon counter technology still has limited processing power for high count rates. This may limit the speed and throughput in practical applications of the scheme.
Extensibility and integration: quantum random number generation schemes based on optical platforms face some challenges in terms of scalability and integration. Achieving large-scale deployment and integration requires solving the problems of interoperability of devices and systems, resource management, communication protocols, and the like.
Therefore, according to the embodiment of the application, the Gaussian distribution variance of the quantum noise in the time domain can be obtained by acquiring the current time domain signal passing through the diode with the electron tunneling effect and analyzing the frequency spectrum of the current time domain signal. According to the probability density diagram of the current time domain signal, a first random number sequence is obtained, and based on Gaussian distribution variance of quantum noise in the time domain, quantum random numbers can be extracted from the first random number sequence. The scheme does not relate to optical devices and optical systems, and the convenience of quantum random number generation is improved.
A circuit diagram for generating quantum noise according to an embodiment of the present application is illustrated in fig. 1. As shown in fig. 1, the circuit includes a diode 110 and a dc regulated power supply 120.
Wherein the dc regulated power supply 120 and the diode 110 are connected in series. The diode 110 is reverse biased by the dc regulated power supply 120. The dc regulated power supply 120 may be a small regulated power supply that provides a voltage of, for example, 5-6V. The reverse voltage applied across the diode is set according to a preset range. The preset range is a range related to the reverse breakdown voltage of the diode, including the reverse breakdown voltage, and a voltage slightly greater or slightly less than the reverse breakdown voltage.
The diode 110 may be any common diode, and its material, size, etc. are not limited. Diodes with fewer impurities and low noise can be selected to generate a current time domain signal with a better performance index.
With the dc regulated power supply 120 turned on, the diode 110 under reverse bias will generate quantum tunneling effect under the action of reverse voltage. Quantum tunneling (quantum tunneling effect, QTE) refers to the quantum behaviour of microscopic particles like electrons able to penetrate or cross a potential barrier, although the height of the potential barrier is greater than the total energy of the particle. As can be seen from the above analysis, a noise component is included in the current passing through the diode 110, and includes non-quantum noise and quantum noise, and the quantum noise is generated by the electron tunneling effect. The current can be used as an entropy source to extract the quantum random number. These quantum random numbers are generated based on quantum effects and are unpredictable and thus truly random number sequences.
In the series circuit shown in fig. 1, an ammeter 130 meeting the circuit requirement may be connected in series to monitor the current time domain signal passing through the diode 110 in real time, and the output end of the ammeter is connected to a spectrum analyzer 140 to obtain a spectrogram of the current time domain signal. Specifically, the range and accuracy of the ammeter 130 need to meet the design requirements of the circuit, and the measurement speed of the ammeter 130 needs to be matched with the rate of generating the quantum random number. The range of the spectrum analyzer 140 needs to reach the order of OHz to GHz.
A current time domain signal diagram provided by an embodiment of the present application is shown in fig. 2. As shown in fig. 2, the trend of the current value measured by the ammeter 130 in fig. 1 over time is shown in the current time domain signal diagram. From this current time domain signal plot, the current average (average) and the variance due to noise (noise) can be calculated.
As can be seen from fig. 2, the current through the diode fluctuates around the average value of the current due to the quantum tunneling effect. The spectrogram corresponding to the noise component can be obtained based on the current time domain signal, the duty ratio of the quantum noise and the non-quantum noise is analyzed, and the Gaussian distribution variance of the quantum noise in the time domain is obtained. And then extracting the quantum random number from the current data center according to the Gaussian distribution variance of the quantum noise in the time domain.
Therefore, after the current time domain signal passing through the diode 110 with the electron tunneling effect is obtained, a programmable component such as a computer or FPGA is used to record and analyze the current time domain signal and the processing result of the spectrum analyzer, so as to obtain the quantum random number meeting the design requirement.
Next, based on the contents shown in fig. 1 to 2, a detailed description is given of a quantum random number generation method based on a diode electron tunneling effect provided in an embodiment of the present application.
Fig. 3 shows a flowchart of a quantum random number generation method based on diode electron tunneling effect according to an embodiment of the present application. As shown in fig. 3, the method comprises the steps of:
in step S301, a current time domain signal passing through a diode generating an electron tunneling effect is obtained. The noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by the electron tunneling effect of the diode.
As in the circuit arrangement shown in fig. 1, the diode can be made to generate electron tunneling effect by applying a reverse dc voltage across the diode. In the current time domain signal through the diode, a current component and a noise component are included. Included in the noise component are non-quantum noise and quantum noise generated by the electron tunneling effect of the diode. Common non-quantum noise includes circuit noise, ambient noise, and the like. The noise is in the category of thermal noise, and the instantaneous value of the noise accords with Gaussian normal distribution.
Step S302, analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component.
Step S303, calculating to obtain Gaussian distribution variance of the quantum noise in a time domain based on the frequency spectrum amplitude of the target frequency spectrum region in the frequency spectrum diagram. The spectral features of the target spectral region are only correlated with non-quantum noise.
As shown in the circuit arrangement of fig. 1, the current data of a period of time can be measured and recorded by monitoring the current passing through the diode in real time by connecting an ammeter meeting the circuit requirement in series.
Fig. 4 shows a probability density graph of a current time domain signal according to an embodiment of the present application. As shown in fig. 4, curve 1 represents the current probability density curve through the diode. The horizontal axis corresponding to curve 1 represents the value of current that may pass through the diode, and the vertical axis represents the probability density of occurrence of a particular current value. As can be seen from fig. 4, based on the effect of the stabilizing electric field exerted by the voltage stabilizing circuit, the instantaneous value of the measured and recorded current time domain signal also conforms to the gaussian normal distribution.
Then, the frequency spectrum of the current time domain signal is analyzed by utilizing a frequency analyzer, so that a spectrogram corresponding to the noise component can be obtained. In fig. 5, noise spectra corresponding to different current time domain signals obtained from current data of different time periods are recorded, different curves represent the noise spectra of different time periods, the horizontal axis is frequency (GHz), and the vertical axis is intensity (dBm). As can be seen from fig. 5, the trend of the spectrum curves corresponding to the respective noise spectrums is substantially the same. Analysis of any of the noise spectral curves having substantially the same trend shows that it can be decomposed into a plurality of different noises in the frequency domain.
Fig. 6a shows a noise subcomponent spectrum diagram of a current time domain signal according to an embodiment of the present application. As shown in fig. 6a, for any of the noise spectrum curves shown in fig. 5, flicker noise (1/fnois), random telegraph noise (random telegraph noise, RT noise), thermal noise (thermal noise), shot noise (shot noise) and quantum noise (quantum noise) can be decomposed in the frequency domain. As can be seen from fig. 6a, the horizontal axis of the spectrum of the noise subcomponent is frequency and the vertical axis is intensity, and only the non-quantum noise subcomponent exists before the frequency value gradually increases to a specific value. In the spectral region after the frequency reaches this particular value, almost only quantum noise subcomponents are present.
Fig. 6b shows a non-quantum noise spectrum of a current time domain signal according to an embodiment of the present application. Fig. 6b shows a spectrum of non-quantum noise, including flicker noise, random telegraph noise, thermal noise, and shot noise, corresponding to the spectrum of each of the noise subcomponents in fig. 6 a. As shown in fig. 6b, the horizontal axis of the non-quantum noise curve is frequency, the vertical axis is intensity, and the non-quantum noise gradually disappears in a spectral region after the frequency reaches a certain value.
As can be seen by comparing fig. 5, fig. 6a and fig. 6b, there is a flat spectral curve in the middle of any of the noise spectral curves in fig. 5, which includes only non-quantum noise, and almost only quantum noise subcomponents in the spectral region after the flat curve ends. Thus, the quantum noise signal can be extracted from the noise component.
As can be seen from fig. 6a, the frequency corresponding to the quantum noise is relatively high, and in practice, it tends to reach 10GHz or more. If the quantum noise signal is extracted directly through recording and integrating, high requirements are put on the precision and the measuring range of the frequency analyzer, and the operability is low.
In the region of the flat spectrum curve in the middle of the noise spectrum curve, namely in the target spectrum region, the frequency corresponding to the non-quantum noise is lower, and the frequency spectrum change trend is more gentle. Computer simulation and signal extraction of non-quantum noise is easier to implement. Therefore, the Gaussian distribution variance of the quantum noise in the time domain can be calculated based on the frequency spectrum amplitude of the target frequency spectrum region in the frequency spectrogram.
Specifically, the signal intensity ratio gamma of the quantum noise and the non-quantum noise in the noise component is obtained through estimation.
And secondly, obtaining the power spectrum density of the non-quantum noise in the target spectrum region based on the spectrum amplitude of the target spectrum region. The method comprises the steps of determining any frequency in a target frequency spectrum region and determining the frequency spectrum amplitude corresponding to the frequency in any noise frequency spectrum curve shown in fig. 5. And obtaining the power spectral density of the non-quantum noise in the target frequency spectrum region based on the frequency spectrum amplitude according to theoretical analysis. As can be seen from fig. 6b, the power spectral density of the non-quantum noise corresponding to any frequency within the target spectral region is the same.
And then according to the signal intensity ratio and the power spectrum density of the non-quantum noise in the target spectrum region, calculating to obtain the Gaussian distribution variance of the quantum noise in the time domain as follows:
as shown in formula (1), gamma is the signal intensity ratio, S (0) is the power spectral density of the non-quantum noise in the target spectral region, ω min 、ω max Is a preset frequency value contained in a specified spectral region. Omega min 、ω max The setting of these two values determines the interval in which the integration of the formula (1) is performed, and in general, the larger the interval is, the larger the result obtained by the integration of the formula (1) is, that is, the larger the variance of the quantum noise is, the more the corresponding quantum randomness is, and the larger the corresponding integration calculation amount is. If the integral is performed by taking the whole gentle spectrum curve area, the calculated quantum randomness is the most, and the quantity of the finally extracted quantum random number is moreMany. If only any sub-area is taken for integration, the calculated quantum randomness is less, and the quantum random number is less. Thus ω min 、ω max The setting of the two values affects the generation rate of random numbers and the calculation rate of integral, and omega can be calculated according to actual requirements min 、ω max And (5) reasonably setting.
At normal temperature, the signal strength ratio γ tends to stabilize based on stable ambient temperature and circuit operating voltage, determined only by the nature of the diode, and can generally be considered as a constant of 1. Omega min 、ω max The value of (2) can be arbitrarily set in a specified spectrum region.
Since the instantaneous value of the quantum noise corresponding to the time domain is in gaussian normal distribution, the average value thereof is the same as the average value of the current time domain signal passing through the diode. Since quantum noise is part of the time domain signal of the current passing through the diode, the probability density curve of quantum noise can be shown together in fig. 4, specifically as shown by curve 2 in fig. 4.
Step S304, a first random number sequence is obtained according to the correlation of the current value sampled for a plurality of times and the probability density map of the current time domain signal. And extracting quantum random numbers from the first random number sequence based on Gaussian distribution variance of quantum noise in a time domain.
Based on the probability density map of the current time domain signal, the process of obtaining the first random number sequence is as follows:
firstly, a current distribution area represented by a curve 1 in fig. 4 is longitudinally divided into a plurality of subareas, and each subarea corresponds to a current value interval of different ranges. Secondly, random numbers with the same number of bits are respectively arranged for each subarea. Wherein the random numbers in the different sub-regions are different.
A graph of a probability density curve of a current time domain signal according to an embodiment of the present application is shown in fig. 7. As shown in fig. 7, the area with current distribution in the probability density curve is longitudinally divided into 8 areas, and each area is provided with different 3bit random numbers from left to right, which are as follows: 000, 001, 010, 011, 100, 101, 110, 111.
The first random number sequence can be obtained based on random numbers corresponding to the subareas where the current values obtained by multiple current sampling experiments fall.
Specifically, for each current sampling experiment, a first current value passing through the diode is obtained. And determining a first subarea corresponding to the current value interval to which the first current value belongs in the probability density map. And taking the random number corresponding to the first subarea as the random number obtained by the current sampling experiment. And finally, determining a first random number sequence based on all random numbers obtained by multiple current sampling experiments.
Next, the first random number sequence is post-processed based on the minimum entropy to obtain a quantum random number. The minimum entropy gives the upper limit of the uniformly distributed random numbers that can be extracted from the random variable X. For example, for an initial random variable X of n bits, if its minimum entropy is k, only k bits of ideal random numbers can be extracted from n bits of data at most.
Thus, a first minimum entropy of the first random number sequence is calculated. Based on the probability density map of quantum noise shown in curve 2 in fig. 4, a second minimum entropy of the quantum random number is calculated. A quantum random number is then extracted from the first sequence of random numbers based on the first minimum entropy and the second minimum entropy.
The method is applied to a Toeplitz matrix in a post-processing algorithm, wherein the Toeplitz matrix is a hash function, and the original random number sequence with the length of m is multiplied by the Toeplitz matrix with the size of m multiplied by n to obtain a post-processed random sequence with the length of n.
Specifically, the number of rows m of the Toeplitz matrix is determined using a first minimum entropy, and the number of columns n of the Toeplitz matrix is determined using a second minimum entropy. By constructing a Toeplitz matrix according to the m×n scale, quantum random numbers with preset lengths can be extracted from the first random number sequence based on the Toeplitz matrix.
Therefore, the scheme utilizes the current generated by the tunneling phenomenon inside the diode when the reverse voltage is applied as an entropy source to extract the quantum random number. The intensity of the non-quantum noise is obtained by performing a spectral analysis on the measured current. And the intensity ratio of the quantum noise to the non-quantum noise is obtained by utilizing the working environment and the diode property, and the distribution of the quantum noise is restored. And obtaining minimum entropy by utilizing the distribution of quantum noise and the noise component obtained by actual measurement, and further extracting the random number to obtain the final quantum random number.
Compared with the optical platform scheme, the scheme has the following advantages:
low cost: the common commercial circuit devices are used for generating random numbers, so that the cost is lower.
The system is simple: as can be seen from fig. 1, the circuit related to the scheme is very simple, convenient to maintain, and very direct in operation and reading.
The speed is not limited: because photoelectric conversion and photon measurement are not needed, the generation speed of the scheme only depends on the measurement speed of an ammeter, and the scheme is not limited in principle.
High integration and extensibility: because this scheme is whole only to relate to electronic components, need not to carry out photoelectric conversion, consequently more convenient integration is on the chip to do benefit to the extension to the circuit.
Good randomness: the quantum random number finally obtained by the scheme is a true quantum random number with theoretical guarantee, and has true randomness.
It should be noted that while in the above embodiments, the operations of the methods of the embodiments of the present disclosure are described in a particular order, this does not require or imply that the operations must be performed in that particular order or that all of the illustrated operations be performed in order to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
Based on the method in the above embodiment, fig. 8 illustrates an exemplary quantum random number generating device based on diode electron tunneling effect according to an embodiment of the present application. As shown in fig. 8, the quantum random number generation device 800 includes:
an acquisition unit 810 for acquiring a current time domain signal passing through a diode in which an electron tunneling effect occurs; the noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by the electron tunneling effect of the diode.
The processing unit 820 is configured to analyze the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component.
The processing unit 820 is further configured to calculate, based on the spectral amplitude of the target spectral region in the spectrogram, a gaussian distribution variance of the quantum noise in the time domain. Wherein the spectral features of the target spectral region are only correlated with non-quantum noise.
The processing unit 820 is further configured to obtain a first random number sequence according to correlation between the current value sampled multiple times and the probability density map of the current time domain signal, and extract a quantum random number from the first random number sequence based on a gaussian distribution variance of quantum noise in a time domain.
Based on the method in the above embodiment, the embodiment of the application provides an electronic device. The electronic device may include: at least one memory for storing a program; at least one processor for executing the programs stored in the memory. Wherein the processor is adapted to perform the method described in the above embodiments when the program stored in the memory is executed. By way of example, the electronic device may be a cell phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, server, ultra-mobile personal computer (UMPC), netbook, as well as a cellular telephone, personal digital assistant (personal digitalassistant, PDA), augmented reality (augmented reality, AR) device, virtual Reality (VR) device, artificial intelligence (artificial intelligence, al) device, wearable device, in-vehicle device, smart home device, and/or smart city device, and the specific type of electronic device is not particularly limited by the embodiments of the present application.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
It will be appreciated that the various numerical numbers referred to in the embodiments of the present application are merely for ease of description and are not intended to limit the scope of the embodiments of the present application. It should be understood that, in the embodiment of the present application, the sequence number of each process does not mean the sequence of execution, and the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application in further detail, and are not to be construed as limiting the scope of the application, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the application.
Claims (10)
1. A method for generating a quantum random number based on diode electron tunneling effect, the method comprising:
acquiring a current time domain signal passing through a diode with an electron tunneling effect; the noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by electron tunneling effects of the diode;
analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component;
based on the spectrum amplitude of the target spectrum area in the spectrogram, calculating to obtain Gaussian distribution variance of the quantum noise in a time domain; spectral features of the target spectral region are correlated only with the non-quantum noise;
and obtaining a first random number sequence according to the correlation of the current value sampled for multiple times and the probability density map of the current time domain signal, and extracting the quantum random number from the first random number sequence based on the Gaussian distribution variance of the quantum noise in the time domain.
2. The method of claim 1, wherein the diode is reverse biased by a dc regulated power supply, the dc regulated power supply and the diode being connected in series, the voltage applied across the diode being set according to a predetermined range, the predetermined range being related to the reverse breakdown voltage of the diode.
3. The method according to claim 1, wherein the calculating, based on the spectral magnitudes of the target spectral regions in the spectrogram, a gaussian distribution variance of the quantum noise in a time domain includes:
estimating the signal intensity ratio of the quantum noise and the non-quantum noise in the noise component;
obtaining the power spectral density of the non-quantum noise in the target spectral region based on the spectral amplitude of the target spectral region;
according to the signal intensity ratio and the power spectrum density of the non-quantum noise in the target spectrum region, the Gaussian distribution variance of the quantum noise in the time domain is calculated as follows:
wherein gamma is the signal intensity ratio, S (0) is the power spectral density of the non-quantum noise in the target spectral region, omega min 、ω max Is a preset frequency value contained within the specified spectral region.
4. The method of claim 1, wherein the deriving a first random number sequence from the correlation of the current values of the plurality of samples and the probability density map of the current time domain signal comprises:
longitudinally dividing a current distribution area of the probability density map into a plurality of subareas; each of the plurality of sub-regions corresponds to a current value interval of different ranges;
setting random numbers with the same digits for each of the plurality of subareas respectively; wherein the random numbers in the different sub-regions are different;
and obtaining the first random number sequence based on random numbers respectively corresponding to the subareas where the current values obtained by multiple current sampling experiments fall.
5. The method according to claim 4, wherein the obtaining the first random number sequence based on random numbers corresponding to the sub-regions where the plurality of current values obtained through the plurality of current sampling experiments fall respectively includes:
for each current sampling experiment,
acquiring a first current value passing through the diode;
determining a first subarea corresponding to a current value interval to which the first current value belongs in the probability density map;
taking the random number corresponding to the first subarea as the random number obtained by the current sampling experiment;
and determining the first random number sequence based on all random numbers obtained by the current sampling experiments.
6. The method of claim 1, wherein the extracting the quantum random number from the first random number sequence based on a gaussian distribution variance of the quantum noise in a time domain comprises:
obtaining a first minimum entropy of the first random number sequence through calculation;
determining a probability density map of the quantum noise based on a Gaussian distribution variance of the quantum noise in a time domain;
calculating a second minimum entropy of the quantum random number based on the probability density map of the quantum noise;
the quantum random number is extracted from the first sequence of random numbers based on the first minimum entropy and the second minimum entropy.
7. The method of claim 6, wherein the extracting the quantum random number from the first sequence of random numbers based on the first minimum entropy and the second minimum entropy comprises:
determining a number of rows of a Toepli tz matrix using the first minimum entropy, and determining a number of columns of the Toeplitz matrix using the second minimum entropy;
constructing the Toeplitz matrix based on the number of rows and the number of columns;
the quantum random number is extracted from the first random number sequence based on the Toeplitz matrix.
8. A circuit for generating quantum noise, the circuit comprising a diode and a dc regulated power supply, the dc regulated power supply applying a reverse bias to the diode, the dc regulated power supply and the diode being connected in series, the voltage applied across the diode being set according to a predetermined range, the predetermined range being related to a reverse breakdown voltage of the diode; the diode generates electron tunneling effect under the action of reverse voltage;
the current time domain signal passing in the diode includes a noise component including the non-quantum noise and quantum noise, the quantum noise being generated by the electron tunneling effect;
the current time domain signal is used for obtaining a spectrogram corresponding to the noise component; the spectrum amplitude of the target spectrum area in the spectrogram is used for obtaining Gaussian distribution variance of the quantum noise in a time domain; spectral features of the target spectral region are correlated only with the non-quantum noise;
the probability density map of the current time domain signal is used for obtaining a first random number sequence, and the Gaussian distribution variance of the quantum noise in the time domain is used for extracting the quantum random number from the first random number sequence.
9. A quantum random number generation device based on diode electron tunneling effect, the device comprising:
the acquisition unit is used for acquiring a current time domain signal passing through the diode with the electron tunneling effect; the noise component of the current time domain signal includes non-quantum noise and quantum noise, the quantum noise being generated by electron tunneling effects of the diode;
the processing unit is used for analyzing the frequency spectrum of the current time domain signal to obtain a spectrogram corresponding to the noise component;
the processing unit is further used for calculating Gaussian distribution variance of the quantum noise in a time domain based on the frequency spectrum amplitude of the target frequency spectrum area in the frequency spectrogram; spectral features of the target spectral region are correlated only with the non-quantum noise;
the processing unit is further configured to obtain a first random number sequence according to correlation between a current value sampled multiple times and a probability density map of the current time domain signal, and extract the quantum random number from the first random number sequence based on a gaussian distribution variance of the quantum noise in a time domain.
10. An electronic device, comprising: at least one memory for storing a program; at least one processor for executing the programs stored in the memory; wherein the processor is adapted to perform the method of any of claims 1-8 when the program stored in the memory is executed.
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