CN111082938B - Method and device for improving quantum key distribution system code rate - Google Patents
Method and device for improving quantum key distribution system code rate Download PDFInfo
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Abstract
The method comprises two stages, namely a model establishing stage and a self-compensation stage, wherein the QKD system quantum key distribution in the model establishing stage works according to the existing working mode, an excitation function network is trained according to a learning algorithm, and the self-compensation stage is entered after the excitation function network is trained. In the self-compensation stage, the quantum key distribution of the QKD system does not work according to the existing working mode any more, the error rate of the QKD system is given by the excitation function network, and the results of all basis vector measurements in the QKD system all participate in the code forming process of the system key, i.e. the system does not need to disclose the comparison result of one group of basis vectors to obtain the error rate, and does not need to discard the data of one group of orthogonal basis measurements, so that the extraction rate of the security key finally obtained by the method is high relative to the initial key, and the code forming rate of the QKD system is high.
Description
Technical Field
The application relates to the technical field of quantum key distribution, in particular to a method and a device for improving the code rate of a quantum key distribution system.
Background
Quantum secret communication is a new communication technology developed in recent years, is a new discipline generated by combining quantum theory and information theory, and realizes unconditional security of communication by using the basic characteristics of quantum physics. Among them, Quantum Key Distribution (QKD) has attracted much attention as a branch of the earliest realization of commercialization in quantum communication technology for more than ten years and has been rapidly developed.
QKD is the design of encryption and decryption schemes using the quantum properties of substances (e.g., photons), and its security is based on the fundamental principles of quantum mechanics rather than the complexity of mathematical calculations. The QKD discovers the existence of eavesdropping by utilizing a Heisenberg uncertainty principle and an unknown quantum state unclonable principle, and theoretically ensures the unconditional security of information. In practical application, the QKD can establish a communication key for both parties without shared secret information in advance by using this principle, and then communicate by using a "one-time pad" cipher certified by shannon, thereby ensuring the communication security of both parties.
Currently, the most commonly used QKD protocol is the BB84 protocol (Bennett and Brassard, 1984). When the BB84 protocol is used for quantum key distribution, the quantum key transmitter (Alice) selects one of two groups of orthogonal basis vectors to encode each time, and the quantum key receiver (Bob) also randomly selects one of the two groups of orthogonal basis vectors to decode each time, namely, 4 single photons with different polarizations are used for transmitting key information, so that the quantum key distribution is completed.
Existing QKD systems obtain a string of initial keys from measurements after Bob receives a photon during the key distribution process, and are in classical communication with Alice. At this time, Bob's measurement basis is completely published, and first, Bob compares basis vectors, then, Bob discards a result generated by selecting an erroneous measurement basis, and then discloses a group of comparison results of basis vectors to calculate an error rate and check the error rate, and the specific steps are as follows: calculating an error rate by disclosing measurements of a set of orthogonal bases; checking whether an eavesdropper exists or not by using the error rate, if the error rate exceeds the tolerance, indicating that the eavesdropper exists, and giving up all contents transmitted by the communication; if the error rate is within the tolerance range, the part of the data exchanged for checking the error rate is discarded (i.e. the data of one set of orthogonal basis measurements is discarded), and the rest of the data is retained. And finally, after the two communication parties carry out privacy enhancement again, the data reserved by Alice and Bob are kept highly consistent, the two transmitting and receiving ends carry out error correction and privacy enhancement through a classical channel, the information which can be obtained by an eavesdropper is changed into invalid, and finally obtained data is used as a password string for encryption.
However, the conventional QKD system discards the part of the data exchanged for checking the error rate, i.e., discards the data of one set of orthogonal basis measurements, and thus the result of one set of orthogonal basis measurements is not used for the QKD system to code, i.e., the extraction rate of the final secure key relative to the initial key is small, thereby resulting in a lower rate of the QKD system to code.
Disclosure of Invention
The application provides a method and a device for improving the code rate of a quantum key distribution system, which aim to solve the problem that the existing QKD system is low in code rate.
The first aspect of the present application provides a method for improving the success rate of a quantum key distribution system, which includes a model establishing stage and a self-compensation stage:
a model establishing stage:
the QKD system performs quantum key distribution, performs basis vector comparison on the key distributed at the previous moment at the current moment, and calculates the error rate of the key distributed at the previous moment;
training an excitation function network through a learning algorithm and the obtained error rate until the excitation function network can predict expected output, and completing model establishment;
and (3) self-compensation stage:
after the model is built, according to the built excitation function network, predicting the error rate of a key distributed at the moment on the QKD system by the excitation function network at the current moment;
and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption after privacy enhancement according to the results of all basis vector measurements.
Preferably, the training of the excitation function network by the learning algorithm and the obtained error rate includes:
when the error rate of the key distributed at the previous moment is obtained, establishing a current excitation function according to the current moment and the error rate, wherein the input layer of the current excitation function comprises the error rate of the key distributed at the previous moment and the output results of all excitation functions obtained by the previous layer;
and repeating the steps, and connecting the obtained excitation functions into an excitation function network according to rules in a learning algorithm until the excitation function network can predict expected output according to the input of the real environment, thereby completing model establishment.
Preferably, after entering the self-compensation phase, the method further comprises:
and comparing the error rate output by the excitation function network with the real error rate given by an error correction module of the QKD system, and returning to the model establishing stage if the difference is greater than a preset threshold value.
Preferably, after entering the self-compensation phase, the method further comprises setting a timer:
and returning to the model establishing stage according to a fixed time period by setting a timer.
Preferably, in the modeling phase, the set of orthogonal basis encoding probabilities that control the QKD system to calculate the error rate are lower than typical;
and in the self-compensation stage, the encoding probabilities of two groups of orthogonal bases of the QKD system are controlled according to actual requirements or quantum communication protocols.
Preferably, the learning algorithm comprises a time-cycle neural network algorithm, an adaptive resonance theory network algorithm, a learning vector quantization network algorithm, a self-organizing feature mapping network algorithm or a recursive network algorithm.
The second aspect of the present application provides an apparatus for improving the code rate of a quantum key distribution system, where the apparatus includes a model building module and a self-compensation module:
a model establishing stage:
the QKD system performs quantum key distribution, and performs basis vector comparison on the key distributed at the last moment at the current moment, so as to obtain the error rate of the key distributed at the last moment;
the model building module is used for training the excitation function network through a learning algorithm and the obtained error rate until the expected output can be predicted by the excitation function network, and building a model;
and (3) self-compensation stage:
after the model is built, the self-compensation module is used for predicting the error rate of a key distributed on the QKD system at a moment by the excitation function network according to the built excitation function network;
and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption after privacy enhancement according to the results of all basis vector measurements.
Preferably, the model building module comprises a training unit:
the training unit is used for training the excitation function network according to a learning algorithm, the learning algorithm comprises that when the error rate of the key distributed at the last moment is obtained, the current excitation function is established according to the current moment and the error rate, and the input layer of the current excitation function comprises the error rate of the key distributed at the last moment and the results output by all the excitation functions obtained by the previous layer;
and repeating the steps, and connecting the obtained excitation functions into an excitation function network according to rules in a learning algorithm until the excitation function network can predict expected output according to the input of the real environment, thereby completing model establishment.
Preferably, the apparatus further comprises an alignment module:
and the comparison module is used for comparing the error rate output by the excitation function network with the real error rate given by error correction in the QKD system in a self-compensation stage, and returning to the model establishing stage if the difference is greater than a preset threshold.
Preferably, the apparatus further comprises a clock module:
and the clock module is used for setting a timer and returning to the model establishing stage according to a fixed time period.
The application provides a method and a device for improving the code rate of a quantum key distribution system, and compared with the prior art, the method and the device have the following advantages:
the method comprises two stages, namely a model establishing stage and a self-compensation stage, wherein the distribution of the quantum key of the QKD system in the model establishing stage works according to the existing working mode, an excitation function network is trained according to a learning algorithm, and the self-compensation stage is started after the excitation function network is trained. In the self-compensation stage, the quantum key distribution of the QKD system does not work according to the existing working mode any more, the error rate of the QKD system is given by the excitation function network, and the results of all basis vector measurements in the QKD system all participate in the code forming process of the system key, i.e. after the system enters the self-compensation stage, the error rate is obtained without disclosing the comparison result of one group of basis vectors, i.e. without discarding the data of one group of orthogonal basis measurements, so that the extraction rate of the security key finally obtained by the method is high relative to the initial key, and the code forming rate of the QKD system is high.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present application;
FIG. 2 is a schematic flow chart of the method of the present application with a return mechanism;
FIG. 3 is another schematic flow diagram of the method of the present application with a return mechanism;
fig. 4 is a schematic structural diagram of the apparatus of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
A first aspect of the present application provides a method for increasing a quantum key distribution system resultant code rate, where the method includes a model building stage and a self-compensation stage, and a specific flow refers to a schematic diagram shown in fig. 1:
a model establishing stage:
the method comprises the steps that S1, a QKD system carries out quantum key distribution according to a mode in the prior art, a key distributed at the last moment is subjected to basis vector comparison at the current moment, the error rate of the key distributed at the last moment is calculated, specifically, Alice in the QKD system sends a quantum signal according to a selected basis vector, Bob in the QKD system decodes the received quantum signal according to the selected basis vector, after receiving all information sent by Alice, the Bob informs Alice by using a classical channel, Alice and Bob carry out basis vector comparison through the classical channel, the QKD system reserves a data sequence obtained by correctly measuring the basis vector, then the error rate of the key distributed at the last moment is calculated, and data with error codes are removed from the reserved data sequence according to the error rate to obtain an original key.
And S2, training the excitation function network by the system through a learning algorithm and the obtained error rate until the excitation function network can predict expected output, namely the excitation function network can predict expected output according to the input of a real environment, and completing model establishment. For example, when the error rate of key distribution at the previous time needs to be obtained at the current time, the error rate of key distribution at the previous time can be automatically output by the excitation function network at the current time without obtaining the error rate through basis vector comparison.
And (3) self-compensation stage:
s3, after the model is built, according to the built excitation function network, predicting the error rate of the key distributed at the moment on the QKD system by the excitation function network at the current moment; and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption through privacy enhancement according to the results of all basis vector measurements.
S4, in the self-compensation stage, the quantum key distribution of the QKD system no longer works according to the existing working mode, the error rate of the QKD system is given by the excitation function network, and the results of all basis vector measurements in the QKD system all participate in the coding process of the system key, so that the QKD system performs the verification work according to the error rate output by the excitation function network, and the QKD system can obtain the cipher string for encryption according to the results of all basis vector measurements and after the secrecy enhancement. Namely, after the system enters a self-compensation stage, the error rate is obtained without disclosing the comparison result of one group of basis vectors and discarding the data of one group of orthogonal basis measurements, so that the extraction rate of the security key finally obtained by the method is high relative to the initial key, and the rate of finished code of the QKD system is high.
The specific steps of training the excitation function network according to the learning algorithm are as follows:
when the error rate of the key distributed at the last moment is obtained, establishing a current excitation function according to the current moment and the error rate, wherein the input layer of the current excitation function comprises the error rate of the key distributed at the last moment; and repeating the steps, and connecting the obtained excitation functions into an excitation function network according to rules in a learning algorithm until the excitation function network can predict expected output according to the input of the real environment, thereby completing model establishment.
For example, one of the modeling processes is as follows, at time t1Then, the error rate obtained is x1Then the excitation function at this time is f (x)1，t1) At time t2Then, the error rate obtained is x2Then the excitation function at this time is f (x)2，t2) And so on until the time is tnThen, the error rate obtained is xnThen the excitation function at this time is f (x)n，tn) When the excitation function network composed of the obtained excitation functions can predict expected output according to the input of the real environment, the QKD system performs key distribution according to the method in the self-compensation stage. When the QKD system enters the self-compensation phase, the error rate is given by the network of excitation functions, e.g., at tn+1When the function relation of the excitation function network is f (x)1····xn，t1····tn) Then according to tn+1And f (x)1····xn，t1····tn) Deriving an error rate xn+1By error rate xn+1And carrying out verification work on the current system.
S5, after entering the self-compensation phase, the method further includes: comparing the error rate output by the excitation function network with the real error rate given by the error correction module of the QKD system, and if the difference is greater than a preset threshold, returning to the model building stage, where the specific process refers to the schematic diagram shown in fig. 2. With the operation of the QKD system, the error rate value given by the excitation function network may deviate significantly from the actual value due to the influence of the QKD system itself and the external environment, and therefore, the excitation function network needs to be re-established or modified. Specifically, after entering the self-compensation stage, the real error rate of the QKD system is given by an error correction module of the QKD system, the QKD system compares the error rate output by the excitation function network with the real error rate given by the error correction module of the QKD system, and if the error rate output by the excitation function network exceeds the tolerance range of the real error rate, the QKD system returns to the model building stage again, and the excitation function network is re-built or modified by using the method of the model building stage according to the selected learning algorithm.
S6, after entering the self-compensation phase, the method further includes setting a timer: by setting a timer, the model building phase is returned according to a fixed time period, and the specific flow refers to the schematic diagram shown in fig. 3. Alternatively, a timer may be used to set an appropriate time based on the QKD system operation, and the model building phase may be returned to each time the self-compensation phase has been running for a set period of time.
In summary, the present application may adopt the method of S5 or S6 when returning to the model building phase after entering the self-compensation phase.
In the modeling stage, the set of orthogonal basis coding probabilities used by the QKD system to calculate the error rate is controlled to be lower than the typical value determined by the system and/or protocol, e.g., 30% lower than the typical value in a QKD system, the error rate is achieved, and the coding rate is higher, i.e., the typical value can be set to 30%. Taking two time states and two phase states as an example, the Alice terminal encodes the phase states according to the time basis vector encoding time states and the phase basis vector encoding time states, wherein the sending time state is | t |0Is greater than or | t1Transmits a phase state of
（|t0〉+|t1Is) or
（|t0〉-|t1Bob randomly selects one of the time basis vector and the phase basis vector to decode the received signal, performs basis vector comparison after decoding, and discloses the phase basis vector to obtain an error rate, thereby performing data measurement by using the time basis vectorAnd (6) row checking. Therefore, the data obtained by measuring the time basis vector finally participate in the coding, while the phase basis vector is disclosed for calculating the error rate, and the data obtained by measuring the phase basis vector does not participate in the coding for obtaining an absolutely safe key. Therefore, in order to have a higher code rate, the probability of phase-based vector coding is controlled to be below 30%. The preferred probability of phase basis vector encoding is 25% -30%, the probability of time basis vector encoding is 65% -70%, and the probability of vacuum state encoding is 0% -5%.
And in the self-compensation stage, the encoding probabilities of two groups of orthogonal bases of the QKD system are controlled according to actual requirements or quantum communication protocols. After the self-compensation stage is started, the error rate is given through the excitation function network, and the error rate does not need to be calculated by disclosing the phase basis vector, so that data obtained by measuring the phase basis vector and data obtained by measuring the time basis vector both participate in code forming, and therefore, the probability of phase basis vector coding, the probability of time basis vector coding and the probability of vacuum state coding at the stage can all select the coding probability according to actual requirements or protocol regulations, but the overall trend is biased to code forming, namely the system judges the code forming rate of the phase basis vector and the code forming rate of the time basis vector according to the code forming result, so that the probability of phase basis vector coding or the probability of time basis vector coding is correspondingly increased.
It should be noted that, the method of the present application is applicable to a plurality of intensity modulation schemes, for example, four light intensity schemes and five light intensity schemes, and if the measurement result of the basis X vector in the QKD system is used for calculating the error rate and the measurement result of the basis Z vector is used for coding, the following specific manner is referred to:
for example, the four light intensity schemes are respectively the signal state intensity of the Z-base vector, the decoy state intensity of the X-base vector and the vacuum state intensity, in the model building stage under the intensity system, the measurement result of the decoy state intensity of the X-base vector can be used for calculating the error rate and building the model, and the measurement result of the signal state intensity of the Z-base vector and the measurement result of the decoy state intensity of the Z-base vector are used for code formation; after entering the self-compensation stage, the error rate is given by the established model, and the measurement results of the signal state intensity of the Z basis vector, the decoy state intensity of the Z basis vector and the decoy state intensity of the X basis vector can be used for code forming.
For example, five light intensity schemes are respectively signal state intensity of the Z-base vector, decoy state intensity of the Z-base vector, signal state intensity of the X-base vector, decoy state intensity of the X-base vector, and vacuum state intensity, in the model building stage under the intensity system, the measurement results of the decoy state intensity of the Z-base vector and/or the decoy state intensity of the X-base vector can be used for calculating an error rate and building a model, and the measurement results of the signal state intensity of the Z-base vector and the decoy state intensity of the Z-base vector can be used for coding; and after entering the self-compensation stage, the measurement results of the signal state intensity of the Z basis vector, the decoy state intensity of the Z basis vector and the decoy state intensity of the X basis vector can be used for forming codes.
Therefore, in summary, the method of the present application can be applied to various QKD systems, no matter what quantum communication protocols such as BB84 protocol, B92 protocol, and tri-state protocol are selected by the QKD system, no matter what basis loss is selected, no matter what intensity schemes are selected, the method of the present application can be used, an excitation function network is learned through a model establishing stage according to a system environment and parameters, then a self-compensation stage is entered, the QKD system gives an error rate according to the learned excitation function network, the QKD system uses the error rate given by the excitation function network, and information that originally needs to be disclosed for obtaining the error rate does not need to be disclosed at this time, so that the information can be used for coding, and thus the coding rate of the quantum key distribution system can be improved by using the method of the present application.
The learning algorithm comprises a time cycle neural network algorithm, an adaptive resonance theory network algorithm, a learning vector quantization network algorithm, a self-organizing feature mapping network algorithm or a recursive network algorithm. The algorithms can be applied to the application, and are used for training a system in a model building stage to obtain an excitation function network through deep learning.
A second aspect of the present application provides an apparatus for increasing a quantum key distribution system coding rate, where the apparatus includes a model building module and a self-compensation module, and a specific structure refers to a schematic diagram shown in fig. 4:
a model establishing stage:
the QKD system performs quantum key distribution, performs basis vector comparison on the key distributed at the previous moment at the current moment, and calculates the error rate of the key distributed at the previous moment;
the model building module is used for training the excitation function network according to a learning algorithm until the excitation function network can predict expected output according to the input of a real environment, and building a model, wherein the input of the excitation function comprises time and an error rate;
and (3) self-compensation stage:
after the model is built, the self-compensation module is used for predicting the error rate of a key distributed on the QKD system at a moment by the excitation function network according to the built excitation function network;
and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption through privacy enhancement according to the results of all basis vector measurements.
Preferably, the model building module comprises a training unit:
the training unit is used for training the excitation function network according to a learning algorithm, the learning algorithm comprises that when the error rate of the key distributed at the last moment is obtained, the current excitation function is established according to the current moment and the error rate, and the input layer of the current excitation function comprises the error rate of the key distributed at the last moment and the results output by all the excitation functions obtained by the previous layer;
and repeating the steps to obtain the excitation function network until the excitation function network can predict expected output according to the input of the real environment, and finishing model establishment.
Preferably, the apparatus further comprises an alignment module:
and the comparison module is used for comparing the error rate output by the excitation function network with the real error rate given by the error correction module of the QKD system in a self-compensation stage, and returning to the model establishing stage if the difference is greater than a preset threshold value.
Preferably, the apparatus further comprises a clock module:
and the clock module is used for setting a timer and returning to the model establishing stage according to a fixed time period.
The present application has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to limit the application. Those skilled in the art will appreciate that various equivalent substitutions, modifications or improvements may be made to the presently disclosed embodiments and implementations thereof without departing from the spirit and scope of the present disclosure, and these fall within the scope of the present disclosure. The protection scope of this application is subject to the appended claims.
Claims (10)
1. A method for improving the code rate of a quantum key distribution system is characterized by comprising a model establishing stage and a self-compensation stage:
a model establishing stage:
the QKD system performs quantum key distribution, performs basis vector comparison on the key distributed at the previous moment at the current moment, and calculates the error rate of the key distributed at the previous moment;
training an excitation function network through a learning algorithm and the obtained error rate until the excitation function network can predict expected output, and completing model establishment;
and (3) self-compensation stage:
after the model is built, according to the built excitation function network, predicting the error rate of a key distributed at the moment on the QKD system by the excitation function network at the current moment;
and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption after privacy enhancement according to the results of all basis vector measurements.
2. The method of claim 1, wherein training the excitation function network through a learning algorithm and the obtained error rate until the excitation function network predicts the expected output comprises:
when the error rate of the key distributed at the previous moment is obtained, establishing a current excitation function according to the current moment and the error rate, wherein the input layer of the current excitation function comprises the error rate of the key distributed at the previous moment and the output results of all excitation functions obtained by the previous layer;
and repeating the steps, and connecting the obtained excitation functions into an excitation function network according to rules in a learning algorithm until the excitation function network can predict expected output according to the input of the real environment, thereby completing model establishment.
3. The method for increasing the bitrate of a quantum key distribution system according to claim 2, wherein after entering the self-compensation phase, the method further comprises:
and comparing the error rate output by the excitation function network with the real error rate given by an error correction module of the QKD system, and returning to the model establishing stage if the difference is greater than a preset threshold value.
4. The method for increasing the bitrate of a quantum key distribution system according to claim 2, wherein after entering the self-compensation phase, the method further comprises setting a timer:
and returning to the model establishing stage according to a fixed time period by setting a timer.
5. The method for improving the coding rate of a quantum key distribution system according to claim 1, wherein in the model building phase, a set of orthogonal basis coding probabilities used by the QKD system to calculate the error rate is controlled to be lower than a typical value;
and in the self-compensation stage, the encoding probabilities of two groups of orthogonal bases of the QKD system are controlled according to actual requirements or quantum communication protocols.
6. The method for improving the coding rate of a quantum key distribution system according to any one of claims 1 to 5, wherein the learning algorithm comprises a time-cycled neural network algorithm, an adaptive resonance theory network algorithm, a learning vector quantization network algorithm, a self-organized feature mapping network algorithm, or a recursive network algorithm.
7. The device for improving the code rate of the quantum key distribution system is characterized by comprising a model establishing module and a self-compensation module:
a model establishing stage:
the QKD system performs quantum key distribution, performs basis vector comparison on the key distributed at the previous moment at the current moment, and calculates the error rate of the key distributed at the previous moment;
the model building module is used for training the excitation function network through a learning algorithm and the obtained error rate until the expected output can be predicted by the excitation function network, and building a model;
and (3) self-compensation stage:
after the model is built, the self-compensation module is used for predicting the error rate of a key distributed on the QKD system at a moment by the excitation function network according to the built excitation function network;
and the QKD system checks according to the error rate output by the excitation function network, and obtains a password string for encryption after privacy enhancement according to the results of all basis vector measurements.
8. The apparatus for increasing the bitrate of a quantum key distribution system according to claim 7, wherein the model building module comprises a training unit:
the training unit is used for training the excitation function network according to a learning algorithm, the learning algorithm comprises that when the error rate of the key distributed at the last moment is obtained, the current excitation function is established according to the current moment and the error rate, and the input layer of the current excitation function comprises the error rate of the key distributed at the last moment and the results output by all the excitation functions obtained by the previous layer;
and repeating the steps, and connecting the obtained excitation functions into an excitation function network according to rules in a learning algorithm until the excitation function network can predict expected output according to the input of the real environment, thereby completing model establishment.
9. The apparatus for increasing the bitrate of a quantum key distribution system according to claim 8, wherein the apparatus further comprises a comparison module:
and the comparison module is used for comparing the error rate output by the excitation function network with the real error rate given by the error correction module of the QKD system in a self-compensation stage, and returning to the model establishing stage if the difference is greater than a preset threshold value.
10. The apparatus for increasing the bitrate of a quantum key distribution system according to claim 8, wherein the apparatus further comprises a clock module:
and the clock module is used for setting a timer and returning to the model establishing stage according to a fixed time period.
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| CN112134683B (en) * | 2020-09-22 | 2022-02-15 | 中南大学 | Attack detection method of discrete variable quantum key distribution system |
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| CN115941059B (en) * | 2023-01-03 | 2023-09-19 | 北京百度网讯科技有限公司 | Quantum communication realization method and device, electronic equipment and medium |
| CN117118614B (en) * | 2023-10-23 | 2024-01-23 | 浙江九州量子信息技术股份有限公司 | Phase drift and phase error online correction method of phase coding QKD system |
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