CN113810186A - Self-adaptive quantum efficiency high-precision real-time prediction method and system - Google Patents
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Abstract
The invention provides a self-adaptive quantum efficiency high-precision real-time prediction method and a self-adaptive quantum efficiency high-precision real-time prediction system, which mainly comprise the following steps: the method comprises the steps of constructing a local dictionary set by using light intensity input into a detector, external current output from the detector under the light intensity and the fluctuation percentage of the number of local oscillator photons input into the detector, performing initial training and combined training on a deep neural network by using the local dictionary set, performing high-precision real-time prediction on the quantum efficiency of a chip CVQKD system by using the trained deep neural network, and accurately evaluating the actual security key rate of the system according to the accurate real-time quantum efficiency prediction result. The method considers the actual safety existing when the chip CVQKD system is applied, and utilizes the performance that the deep neural network can greatly extract the self-adaptive characteristics of the system, thereby providing a method for accurately estimating the quantum efficiency of the chip CVQKD system along with the fluctuation of local oscillator light in real time. The scheme is simple in implementation mode, convenient for large-scale popularization and suitable for large-scale commercial use.
Description
Technical Field
The invention relates to a security vulnerability defense method, in particular to a self-adaptive quantum efficiency high-precision real-time prediction method and a self-adaptive quantum efficiency high-precision real-time prediction system, and particularly relates to a self-adaptive quantum efficiency high-precision real-time prediction method based on light intensity monitoring and a deep neural network.
Background
In the field of quantum cryptography, Quantum Key Distribution (QKD) technology has rapidly developed and achieved enormous efforts in recent years due to its unconditional security based on quantum mechanics guarantees. Quantum key distribution technology is mature at present, and enables authenticated communication parties Alice and Bob to share a secret key through an insecure quantum channel. In particular, this quantum channel can be freely controlled and processed by a potential eavesdropper. At present, quantum key distribution systems are mainly divided into two major categories, namely, discrete-variable quantum key distribution (DVQKD) systems and continuous-variable quantum key distribution (CVQKD) systems. CVQKD systems utilizing weak coherent states and a balanced homodyne detector are well compatible with classical optical communication systems, as compared to DVQKD systems. It is therefore an urgent task to continue to intensively study the CVQKD system and promote its early commercialization. Continuous variable quantum key dichotomy protocols based on gaussian modulated coherent states (GMCS-CVQKD) have been shown to be unconditionally secure under single, collective and coherent attacks. Meanwhile, the CVQKD has made good progress in the long-distance transmission experiment at the level of 100-. In recent years, photonic integration technology provides an important technical approach for solving the problems of miniaturization, cost effectiveness and compatibility of the traditional optical fiber-based CVQKD system in the existing optical communication system. In addition, silicon-based photonic integration is a mature branch of integrated photonics technology, and rapid progress is made in the aspects of quantum sources, detection and the like. In particular, the chip-based silicon-based CVQKD system has recently been first verified in 2m optical fiber, which means that the CVQKD system takes an important step in the direction of integration.
However, since CVQKD does not take into account the actual deficiencies of the system in theoretical security proofs in detail, almost all CVQKD systems may face potential real security risks. Fortunately, the research on the defects that the third party attacker Eve may use to hide the attack has been conducted more thoroughly. However, the latest breakthrough of the CVQKD system, i.e., the chip-based CVQKD system, as a new CVQKD system, may also face serious potential practical safety problems. In recent years, although researchers have raised practical security issues for chip-based CVQKD systems. Unfortunately, the actual security problem studies for chip-based CVQKD are almost blank compared to the mature studies of the actual security of fiber-based CVQKD. In fact, when the size of a CVQKD system is reduced to the on-chip level, it will be very different from a system built using discrete components. This is because many of the previously overlooked effects will be highlighted, which may lead to a system security breach.
Current research on chip-based CVQKD systems focuses mainly on how to physically implement it, but many practical security issues need to be carefully considered at the same time. For example, non-uniform or rough waveguides or heavy doping in integrated detectors can result in non-negligible free carrier absorption and scattering losses, etc. The scholars find that the jitter of local oscillator light in the CVQKD system can cause practical safety problems, but the current academic world only considers the monitoring of the calibration deviation of lens noise and does not consider the influence of the jitter on the quantum efficiency of the detector. However, the change in carrier mobility caused by the minute local oscillation light jitter may eventually cause a change in quantum efficiency. These non-perfection factors, which have not previously been considered in chip-based CVQKD systems, will result in bias in the evaluation of excessive noise and other parameters between legitimate parties. A chip-based CVQKD system may face severe practical security holes.
Fortunately, although the quantum efficiency in the silicon-based integrated detector in the chip-based CVQKD system changes due to the local oscillator light intensity fluctuation, the invention provides a self-adaptive quantum efficiency high-precision real-time prediction method based on light intensity monitoring and a deep neural network, which can carry out high-precision real-time prediction on the quantum efficiency in the integrated detector in the chip CVQKD system, thereby thoroughly preventing the system potential security hole introduced by the local oscillator light fluctuation induced variable quantum efficiency and further accurately evaluating the actual security key rate of the system with strict actual security.
In chinese patent publication No. CN110635895A, a CVQKD transmitting apparatus and method based on self-stabilizing intensity modulation, and a CVQKD system are disclosed, which include a pulse laser, a first beam splitter, an adjustable optical attenuator, a self-stabilizing intensity modulation apparatus, a first phase modulator, a time delay apparatus, and a first polarization beam splitter; the pulse laser is used for generating a periodic pulse sequence; the first beam splitter is used for splitting the pulse sequence; the variable optical attenuator is used for attenuating signals and inputting the signals into the self-stabilizing intensity modulation device; the self-stabilization intensity modulation device comprises a second beam splitter and a second phase modulator; the first phase modulator generates Gaussian modulated signal light from the signal light; the delay device delays the Gaussian modulated signal light and inputs the delayed signal light into the first polarization beam splitter; and the first polarization beam splitter combines the delayed Gaussian modulated signal light and the local oscillator light and outputs the combined signal light and the local oscillator light.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for self-adaptive quantum efficiency high-precision real-time prediction.
The invention provides a high-precision real-time prediction method for self-adaptive quantum efficiency, which comprises the following steps:
step S1: collecting data required for deep neural network training, including light intensity P input to detector0And outputting the external current I of the detector under the light intensityph；
Step S2: calculating the fluctuation percentage of local oscillator photon number input to the detector
Using light intensity P of the input detector0Output the external current I of the detector under the light intensityphAnd the local oscillator photon number fluctuation percentage of the input detector
Constructing a local dictionary set D:
step S3: constructing a deep neural model
Performing initial training on the deep neural network by using the local dictionary set obtained in the step S2, entering the step S4 when the deep neural network meets the judgment condition that the fluctuation percentage of the local oscillator photon number can be accurately predicted according to the light intensity of the input detector, and otherwise, continuing to expand the local dictionary set until the condition is met;
step S4: performing combined training on the deep neural network, finishing the combined training stage when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the light intensity of an input local oscillator, and otherwise, continuing training;
step S5: and performing high-precision real-time prediction on the quantum efficiency of the chip CVQKD system by using the trained deep neural network, and accurately evaluating the actual security key rate of the system according to the accurate real-time quantum efficiency prediction result.
Preferably, the local oscillator photon number fluctuation percentage input to the detector in the step S2
The calculation is as follows:
wherein:
ρ0: bulk density of free carriers;
Veff: an effective volume measure of the region in which the free carriers are located;
m*: the effective mass of the carriers;
frep: the repetition frequency of the chip CVQKD system;
NLO: the number of photons contained in each local oscillation optical pulse;
hv: h is the Planck constant, v is the frequency of the photon, and the product of the two represents the energy of the photon;
obtaining after element replacement:
wherein n represents the refractive index of the germanium material of the silicon-based germanium detector, ε0Represents the dielectric constant in vacuum, c represents the speed of light in vacuum, qeAmount of charge, beta, representing electronsIBRepresents the inter-band absorption coefficient and,
represents the initial quantum efficiency constant, betasRepresenting the scattering absorption coefficient.
Preferably, the determination condition in step S3 is: satisfies epsilon for a given accuracy1And delta1For any satisfaction
Input light intensity ofP0If the mapping result of the deep neural network under the input light intensity satisfies
And then, completing the initial training of the deep neural network.
Preferably, the determination condition in step S4 is: epsilon for a given accuracy2If the following conditions are met:
wherein
Represents
And the second component in the function value is considered to be the end of the deep neural network joint training.
Preferably, in the step S5, the input light intensity is monitored according to the beam splitting
And (3) accurately predicting the quantum efficiency in real time by using the trained deep neural network:
wherein
The invention provides a self-adaptive quantum efficiency high-precision real-time prediction system, which comprises the following modules:
module M1: collecting data required for deep neural network training, including light intensity P input to detector0And outputting the probe under the light intensityExternal current I of the detectorph；
Module M2: calculating the fluctuation percentage of local oscillator photon number input to the detector
Using light intensity P of the input detector0Output the external current I of the detector under the light intensityphAnd the local oscillator photon number fluctuation percentage of the input detector
Constructing a local dictionary set D:
module M3: constructing a deep neural model
Performing initial training on the deep neural network by using a local dictionary set in the module M2, executing the module M4 when the deep neural network meets the judgment condition that the fluctuation percentage of the local oscillator photon number can be accurately predicted according to the light intensity of an input detector, and otherwise, continuing to expand the local dictionary set until the condition is met;
module M4: performing combined training on the deep neural network, finishing the combined training stage when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the light intensity of an input local oscillator, and otherwise, continuing training;
module M5: and performing high-precision real-time prediction on the quantum efficiency of the chip CVQKD system by using the trained deep neural network, and accurately evaluating the actual security key rate of the system according to the accurate real-time quantum efficiency prediction result.
Preferably, the local oscillator photon number fluctuation percentage of the input detector in the module M2
The calculation is as follows:
wherein:
ρ0: bulk density of free carriers;
Veff: an effective volume measure of the region in which the free carriers are located;
m*: the effective mass of the carriers;
frep: the repetition frequency of the chip CVQKD system;
NLO: the number of photons contained in each local oscillation optical pulse;
hv: h is the Planck constant, v is the frequency of the photon, and the product of the two represents the energy of the photon;
obtaining after element replacement:
wherein n represents the refractive index of the germanium material of the silicon-based germanium detector, ε0Represents the dielectric constant in vacuum, c represents the speed of light in vacuum, qeAmount of charge, beta, representing electronsIBRepresents the inter-band absorption coefficient and,
represents the initial quantum efficiency constant, betasRepresenting the scattering absorption coefficient.
Preferably, the decision condition in the module M3 is: satisfies epsilon for a given accuracy1And delta1For any satisfaction
Input light intensity P of0If the mapping result of the deep neural network under the input light intensity satisfies
And then, completing the initial training of the deep neural network.
Preferably, the decision condition in the module M4 is: epsilon for a given accuracy2If the following conditions are met:
wherein
Represents
And the second component in the function value is considered to be the end of the deep neural network joint training.
Preferably, in the module M5, the input light intensity is monitored according to the beam splitting
And (3) accurately predicting the quantum efficiency in real time by using the trained deep neural network:
wherein
Compared with the prior art, the invention has the following beneficial effects:
1. the current chip-based CVQKD system is still in an experimental verification stage and is not in large-scale commercial use. Therefore, the research of the actual safety problem in the chip CVQKD is almost blank, the actual safety existing in the application of the chip CVQKD system is considered, the performance that the deep neural network can greatly extract the self-adaptive characteristics of the system is utilized, the self-adaptive quantum efficiency high-precision real-time prediction method based on light intensity monitoring and the deep neural network is provided, a method is provided for the real-time accurate estimation of the quantum efficiency of the chip CVQKD system along with the fluctuation of local oscillator light, and the leak is thoroughly eliminated from the source.
2. Although some mathematical methods can be adopted to perform rough preliminary estimation on the quantum efficiency of the chip CVQKD system, which changes along with the local oscillator light intensity fluctuation, the method is not always accurate and thorough. Such mathematically based methods often require conservative estimates, which means that the security of obtaining the key rate needs to be guaranteed at the expense of system partial security key rate performance, which is intolerable in many cases. The method can estimate the real quantum efficiency of the system in real time with high precision, so that the quantum efficiency of the system can be estimated in real time with high precision without breaking the system performance fundamentally, and the method has great significance for constructing and guaranteeing the actual safety of the high-performance chip CVQKD.
3. The mathematical essence of the method is based on mathematical modeling of the whole process of a physical mechanism that the quantum efficiency of the integrated detector in the chip CVQKD system changes along with the fluctuation of local oscillator light, so the method is essentially suitable for thoroughly solving the actual safety problem generally faced by the integrated detector in most chip-based continuous variable quantum key distribution systems. Has quite universality.
4. The implementation mode of the scheme is simple, the prediction neural network in the self-adaptive quantum efficiency high-precision real-time prediction method based on the light intensity monitoring and the deep neural network can be popularized on a large scale once being trained, the marginal cost is almost zero, and the method is suitable for large-scale commercial use.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a method in an embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The specific implementation mode of the quantum efficiency attack step of the local oscillator light intensity fluctuation-caused change detector is as follows: eve ignores the influence of the fact that the quantum efficiency of the integrated detector in the receiving end of the silicon-based integrated CVQKD chip system, which is caused by the change of the quantum efficiency of the integrated detector by Alice and Bob due to the local oscillator light intensity fluctuation of the input silicon-based integrated detector, so that partial noise caused by the eavesdropping system can be covered by adopting interception retransmission attack or other attack modes, and the purpose of eavesdropping a part of the security key is realized without being discovered.
A self-adaptive quantum efficiency high-precision real-time prediction method is based on light intensity monitoring and a deep neural network, and refers to FIG. 1, and comprises the following steps:
step S1: collecting data required by deep neural network training, and utilizing beam splitter to input light intensity P of detector0Performing beam splitting monitoring while measuring the light intensity P0External current I of lower output detectorphCollecting a large amount of data
Where i represents the serial number of the data group, for a total of N groups of data.
Step S2: calculating the fluctuation percentage of local oscillator photon number input to the detector
Constructing local dictionary set as follow-up depth spiritPrepared through network training. The calculation method is as follows:
wherein:
ρ0: bulk density of free carriers;
Veff: an effective volume measure of the region in which the free carriers are located;
m*: the effective mass of the carriers;
frep: the repetition frequency of the chip CVQKD system;
NLO: the number of photons contained in each local oscillation optical pulse;
hv: represents the energy of a photon, where h is the Planckian constant and v is the frequency of the photon;
obtaining after element replacement:
wherein n represents the refractive index of the germanium material of the silicon-based germanium detector, ε0Represents the dielectric constant in vacuum, c represents the speed of light in vacuum, qeAmount of charge, beta, representing electronsIBRepresents the inter-band absorption coefficient and,
represents the initial quantum efficiency constant, betasRepresenting the scattering absorption coefficient.
When the input light intensity and the external current collected according to the preparation steps are completed, the fluctuation percentage of the local oscillator photon number of the input detector is calculated
Using "input local oscillator light intensity P0The output external current I under the light intensity of the input local oscillatorphThe fluctuation percentage of the local oscillator photon number under the input local oscillator light intensity
"the elements composed of three dimensional data constitute a set, namely, a local dictionary set D is constructed:
and preparing for initial training and joint training of the subsequent deep neural network. Wherein
Representing the first local dictionary set, the ith element is: when the input light intensity is
When it is in operation, the corresponding output external current is
The input local oscillator light intensity
The lower local oscillator photon number fluctuation percentage is
The superscript dic means an abbreviation for the full name "dictionary".
Step S3: by using stepsThe local dictionary set obtained in step S2 is used to perform initial training on the deep neural network. For the constructed deep neural network model
Initial training is carried out, when the deep neural network meets the judgment condition that the fluctuation percentage of the local oscillator photon number can be accurately predicted according to the light intensity input into the integrated detector, the epsilon of the given precision is met1And delta1For any satisfaction
Input light intensity P of0Under the conditions of
Representing local dictionary concentration and input light intensity P0The difference between the positive and negative values is less than any small positive number delta1If the mapping result of the deep neural network under the input light intensity satisfies the following conditions:
|| ||2representing the Euclidean norm in a vector space, wherein
Representing an input light intensity of P0Under the condition of (1), a two-dimensional real vector sequence consisting of output external current value and local oscillator photon number fluctuation percentage value obtained by prediction of the neural network model, and the vector and the same P0Nearest neighbor
Corresponding vector
The euclidean norm between is less than a given arbitrary small positive number epsilon1(representing the prediction accuracy), the deep neural network completes the initial training phase and enters the joint training phase. Otherwise, continuing to expand the local dictionary set until the condition is metAnd (4) conditions.
Step S4: and performing combined training on the deep neural network, finishing the combined training stage when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the input local oscillator light intensity, and otherwise, continuing training.
And when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the input local oscillator light intensity, completing the joint training stage. I.e. epsilon for a given accuracy2If the following conditions are met:
wherein
Represents
The second component of the function value, in the calculation of the above equation,
the quantum efficiency represented by Alice and Bob is a fixed constant, β, of quantum efficiencyIBRepresents the interband absorption coefficient, betasRepresents the scattering absorption coefficient, betafcRepresents the free carrier absorption coefficient, ρ0Representing the bulk density of free carriers, VeffRepresenting the effective volume measure, m, of the region in which the free carriers are located*Representing the effective mass of the carriers. f. ofrepRepresenting the repetition frequency, N, of the chip CVQKD systemLORepresenting the number of photons contained in each local oscillator light pulse, h being the Planck constant, v being the frequency of the photons, the product of the two representing the energy of the photons,
representing the average initial velocity of the free carrier ensemble before absorption of a photon, n representing the refractive index of the germanium material of the silicon-based germanium detector, epsilon0Representative of trueDielectric constant in air, c represents the speed of light in vacuum, qeRepresents the charge amount of electrons, λ represents the wavelength of the optical signal, and E represents the strength of the electric field in which the free carriers are located. And at this moment, considering that the deep neural network joint training is finished, otherwise, continuing the training.
Step S5: and (3) carrying out high-precision real-time prediction on the quantum efficiency of the chip CVQKD system by using the trained deep neural network. After the combined training is finished, the input light intensity monitored according to the beam splitting
And (3) accurately predicting the quantum efficiency in real time by using the trained deep neural network:
wherein
And according to the accurate real-time quantum efficiency prediction result, accurately evaluating the actual security key rate of the system.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A self-adaptive quantum efficiency high-precision real-time prediction method is characterized by comprising the following steps:
step S1: collecting data required for deep neural network training, including light intensity P input to detector0And outputting the external current I of the detector under the light intensityph；
Step S2: calculating the fluctuation percentage of local oscillator photon number input to the detector
Using light intensity P of the input detector0Output the external current I of the detector under the light intensityphAnd the local oscillator photon number fluctuation percentage of the input detector
Constructing a local dictionary set D:
wherein i represents the serial number of the data group, and N groups of data are provided in total;
step S3: constructing a deep neural model
Performing initial training on the deep neural network by using the local dictionary set obtained in the step S2, entering the step S4 when the deep neural network meets the judgment condition that the fluctuation percentage of the local oscillator photon number can be accurately predicted according to the light intensity of the input detector, and otherwise, continuing to expand the local dictionary set until the condition is met;
step S4: performing combined training on the deep 2-degree neural network, finishing the combined training stage when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the light intensity of an input local oscillator, and otherwise, continuing the training;
step S5: and performing high-precision real-time prediction on the quantum efficiency of the chip CVQKD system by using the trained deep neural network, and accurately evaluating the actual security key rate of the system according to the accurate real-time quantum efficiency prediction result.
2. The adaptive quantum efficiency high-precision real-time prediction method according to claim 1, characterized in that: the local oscillator photon number fluctuation percentage of the input detector in the step S2
The calculation is as follows:
wherein:
ρ0: bulk density of free carriers;
Veff: an effective volume measure of the region in which the free carriers are located;
m*: the effective mass of the carriers;
frep: the repetition frequency of the chip CVQKD system;
NLO: the number of photons contained in each local oscillation optical pulse;
hv: h is the Planck constant, v is the frequency of the photon, and the product of the two represents the energy of the photon;
obtaining after element replacement:
wherein n represents the refractive index of the germanium material of the silicon-based germanium detector, ε0Represents the dielectric constant in vacuum, c represents the speed of light in vacuum, qeAmount of charge, beta, representing electronsIBRepresents the inter-band absorption coefficient and,
represents the initial quantum efficiency constant, betasRepresenting the scattering absorption coefficient.
3. The adaptive quantum efficiency high-precision real-time prediction method according to claim 1, characterized in that: the decision conditions in step S3 are: satisfies epsilon for a given accuracy1And delta1For any satisfaction
Input light intensity P of0If the mapping result of the deep neural network under the input light intensity satisfies
And then, completing the initial training of the deep neural network.
4. The adaptive quantum efficiency high-precision real-time prediction method according to claim 1, characterized in that: the decision conditions in step S4 are: epsilon for a given accuracy2If the following conditions are met:
wherein
Represents
And the second component in the function value is considered to be the end of the deep neural network joint training.
5. The adaptive quantum efficiency high-precision real-time prediction method according to claim 1, characterized in that: in the step S5, the input light intensity is monitored according to the beam splitting
And (3) accurately predicting the quantum efficiency in real time by using the trained deep neural network:
wherein
6. A self-adaptive quantum efficiency high-precision real-time prediction system is characterized by comprising the following modules:
module M1: collecting data required for deep neural network training, including light intensity P input to detector0And outputting the external current I of the detector under the light intensityph；
Module M2: calculating the fluctuation percentage of local oscillator photon number input to the detector
Using light intensity P of the input detector0Output the external current I of the detector under the light intensityphAnd the local oscillator photon number fluctuation percentage of the input detector
Constructing a local dictionary set D:
wherein i represents the serial number of the data group, and N groups of data are provided in total;
module M3: constructing a deep neural model
Performing initial training on the deep neural network by using a local dictionary set in the module M2, executing the module M4 when the deep neural network meets the judgment condition that the fluctuation percentage of the local oscillator photon number can be accurately predicted according to the light intensity of an input detector, and otherwise, continuing to expand the local dictionary set until the condition is met;
module M4: performing combined training on the deep neural network, finishing the combined training stage when the deep neural network meets the judgment condition that the quantum efficiency can be accurately predicted according to the light intensity of an input local oscillator, and otherwise, continuing training;
module M5: and performing high-precision real-time prediction on the quantum efficiency of the chip CVQKD system by using the trained deep neural network, and accurately evaluating the actual security key rate of the system according to the accurate real-time quantum efficiency prediction result.
7. The adaptive quantum efficiency high-precision real-time prediction system of claim 6, wherein: the local oscillator photon number fluctuation percentage of the input detector in the module M2
The calculation is as follows:
wherein:
ρ0: bulk density of free carriers;
Veff: an effective volume measure of the region in which the free carriers are located;
m*: the effective mass of the carriers;
frep: the repetition frequency of the chip CVQKD system;
NLO: the number of photons contained in each local oscillation optical pulse;
hv: h is the Planck constant, v is the frequency of the photon, and the product of the two represents the energy of the photon;
obtaining after element replacement:
wherein n represents the refractive index of the germanium material of the silicon-based germanium detector, ε0Represents the dielectric constant in vacuum, c represents the speed of light in vacuum, qeAmount of charge, beta, representing electronsIBRepresents the inter-band absorption coefficient and,
represents the initial quantum efficiency constant, betasRepresenting the scattering absorption coefficient.
8. The adaptive quantum efficiency high-precision real-time prediction system of claim 6, wherein: the decision conditions in the module M3 are: satisfies epsilon for a given accuracy1And delta1For any satisfaction
Input light intensity P of0If the mapping result of the deep neural network under the input light intensity satisfies
And then, completing the initial training of the deep neural network.
9. The adaptive quantum efficiency high-precision real-time prediction system of claim 6, wherein: the decision conditions in the module M4 are: epsilon for a given accuracy2If the following conditions are met:
wherein
Represents
And the second component in the function value is considered to be the end of the deep neural network joint training.
10. The adaptive quantum efficiency high-precision real-time prediction method according to claim 6, characterized in that: in the module M5, the input light intensity is monitored according to the beam splitting
And (3) accurately predicting the quantum efficiency in real time by using the trained deep neural network:
wherein
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