Signal determination method based on quantum genetic algorithm, quantum computing device based on quantum genetic algorithm, and medium

Abstract

The present invention relates to the technical field of radar signal waveform design. Disclosed are a signal determination method based on a quantum genetic algorithm, a quantum computing device based on a quantum genetic algorithm, and a medium. The method comprises: constructing quantum bits on the basis of the number of phase coding radar signals transmitted by a radar system, the length of the phase coding radar signals transmitted by the radar system, and the number of chromosomes; determining gene sequences of first chromosomes on the basis of all the quantum bits; determining whether a first chromosome meeting a preset termination condition is present in all the first chromosomes; if yes, taking the first chromosome meeting the preset termination condition as a phase coding radar signal transmitted by the radar system; if not, updating all the first chromosomes on the basis of the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle to obtain second chromosomes; replacing all the first chromosomes in each population with all the second chromosomes, returning to the determination step, and ending the operation until the termination condition is met. The local optimal solution is avoided, and the search quality and the search efficiency are improved.

Description

Signal determination method based on quantum genetic algorithm, quantum computing device and medium Technical Field

[0001] The present invention relates to the field of radar signal waveform design, and in particular to a signal determination method based on quantum genetic algorithm, a quantum computing device and a medium. Background Art

[0002] With the development of military technology, the requirements for radar technology are becoming increasingly stringent. Phase-coded signals are now commonly used as radar transmission waveforms. Specifically, a signal determination system determines the phase-coded radar signal. The radar system, connected to the signal determination system, transmits the corresponding phase-coded radar signal and receives the corresponding echo signal to achieve radar detection. The most common phase-coded radar signal is the binary phase-coded radar signal. As a low-power signal with a large time-bandwidth product, the binary phase-coded radar signal has a complex coding pattern, resulting in higher performance and stronger interference resistance. Consequently, as the code length of the binary phase-coded radar signal increases, the process of searching for it becomes more complex. Relying solely on conventional exhaustive search methods for binary phase-coded radar signals is time-consuming and inefficient.

[0003] In related technologies, genetic algorithms are generally used to determine phase-coded radar signals. When using genetic algorithms to determine phase-coded radar signals, the algorithm first generates an appropriate number of individuals in the population based on the radar system, i.e., determines the population size. The algorithm then calculates the fitness of each individual in the population, and based on this fitness, calculates the probability of the individual being selected for the next iteration. Roulette is then used to select the individuals that will enter the next generation. After selection, the next generation is generated through crossover and mutation. These two processes reflect the process of population evolution. Crossover involves exchanging chromosome segments between individuals according to a preset crossover strategy, reconstituting the individuals and entering the next generation. Mutation involves changing segments within an individual according to a certain probability, regenerating the individuals and entering the next generation. The optimal fitness value is recalculated after each iteration, and optimization is terminated until the current fitness value meets the requirements or the maximum number of iterations has been reached. The final result is output as the phase-coded radar signal. However, this method is limited by the number of individuals in the population, that is, the population size. When the population size is small, the search space is reduced, and local optimal solutions appear, which affects the reliability of the genetic algorithm results and cannot obtain the phase-coded radar signal with optimal performance. When the population size is large, the number of calculations increases proportionally, the convergence speed is too slow, and the search efficiency will also be reduced accordingly.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a signal determination method based on quantum genetic algorithm, which improves the search speed, saves resources, and has a faster convergence speed when the population size is the same, thereby improving the search efficiency of phase-coded radar signals.

[0006] To solve the above technical problems, the present invention provides a signal determination method based on a quantum genetic algorithm, which is applied to a signal determination system connected to a radar system. The determination method includes:

[0007] determining a number of phase-coded radar signals transmitted by the radar system and a length of the phase-coded radar signals transmitted by the radar system;

[0008] Constructing P×G×C quantum bits according to determined algorithm operating parameters, wherein the algorithm operating parameters include a population size P, a chromosome number C, a gene number G, a rotation angle θ, a mutation probability, and a termination parameter, wherein the population size P is the number of phase-coded radar signals emitted by the radar system, the gene number G is the length of the phase-coded radar signal emitted by the radar system, and P, C, G, and θ are all greater than 0;

[0009] Determine the gene sequences of P×C first chromosomes based on all the qubits, where each population includes C first chromosomes, and each first chromosome includes G qubits;

[0010] Determining whether there is a first chromosome in all the first chromosomes that meets a preset termination condition; wherein the termination condition is set based on the termination parameter;

[0011] If so, using the first chromosome that meets the preset termination condition as the phase-coded radar signal sent by the radar system;

[0012] If not, all the first chromosomes are updated according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy, and the rotation angle θ to obtain P×C second chromosomes, each of the populations including C second chromosomes, and each of the second chromosomes including G quantum bits;

[0013] All the first chromosomes in each of the populations are replaced with all the second chromosomes, and the step of determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes is entered.

[0014] On the one hand, when the phase-coded radar signal is a binary-phase-coded radar signal, constructing P×G×C quantum bits according to the determined algorithm operating parameters includes:

[0015] Construct P×G×C quantum bits in a uniform superposition state according to the determined algorithm operating parameters;

[0016] Determining gene sequences of P×C first chromosomes based on all of the quantum bits includes:

[0017] Determine the classical bit state corresponding to all the quantum bits after collapse, where the value of the classical bit state is 0 or 1;

[0018] P×C first chromosomes are determined based on all the classical bit states, where the first chromosomes are binary sequences including G of the classical bit states.

[0019] On the one hand, after determining C first chromosomes according to all the classical bit states, the method further includes:

[0020] All collapsed quantum bits are restored to their pre-collapse states based on all the classical bit states.

[0021] On the one hand, all the first chromosomes are updated according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy, and the rotation angle θ to obtain P×C second chromosomes, including:

[0022] Determining m chromosomes to be mutated among all the first chromosomes according to the mutation probability, where m is a positive integer not greater than C;

[0023] Apply the preset X gate to all the chromosomes to be mutated to obtain m third chromosomes. The X gate is:

[0024] According to the preset crossover strategy, quantum catastrophe strategy and the rotation angle θ, Cm first chromosomes and m third chromosomes are updated to obtain P×C second chromosomes.

[0025] On the one hand, the crossover strategy is a full interference crossover strategy.

[0026] On the one hand, according to the preset crossover strategy, quantum catastrophe strategy and the rotation angle θ, Cm first chromosomes and m third chromosomes are updated to obtain P×C second chromosomes, including:

[0027] Merging Cm first chromosomes and m third chromosomes into a new first chromosome;

[0028] Determining the fitness values ​​of all the new first chromosomes according to all the new first chromosomes and a preset fitness relationship expression;

[0029] All the new first chromosomes are updated according to the rotation angle θ, the target chromatin with the maximum fitness value in the previous iteration, and a preset quantum rotating gate expression to obtain P×C fourth chromosomes. The quantum rotating gate expression is:

[0030] Among them, the i-th classical bit state of any chromosome in the new first chromosome is the same as the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, then θ i = 0; when the i-th classical bit state of any chromosome in the new first chromosome is different from the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, the rotation direction is determined based on the quadrant in which any chromosome in the new first chromosome is located, and θ is adjusted according to the rotation direction and the set adjustment angle. i ;

[0031] Determining a target chromosome with the largest fitness value in the next iteration based on the chromosome with the largest fitness value among all the fourth chromosomes and the target chromosome with the largest fitness value in the previous iteration;

[0032] Determine whether the target chromosome with the largest fitness value has not changed for a set number of consecutive times; wherein the set number of times is determined based on the total number of iterations and the set proportion;

[0033] When the target chromosome with the largest fitness value does not change for a set number of consecutive times, the remaining chromosomes of all the fourth chromosomes except the chromosome with the largest fitness value are initialized according to the set replacement ratio to obtain the second chromosome;

[0034] When the target chromosome with the largest fitness value is set to change for a consecutive number of times, the fourth chromosome is used as the second chromosome.

[0035] On the one hand, when the termination parameter includes a fitness threshold, determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes includes:

[0036] Determining the fitness values ​​of all the first chromosomes according to all the first chromosomes and a preset fitness relationship expression;

[0037] Determine whether there is a first chromosome whose fitness value is not less than the fitness threshold among all the first chromosomes;

[0038] If so, determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes;

[0039] If not, it is determined that there is no first chromosome that meets the preset termination condition among all the first chromosomes.

[0040] The present invention also provides a quantum computing device based on a quantum genetic algorithm, which is applied to a signal determination system for phase-coded radar signals. The signal determination system is connected to the radar system. The quantum computing device includes:

[0041] a qubit construction module, configured to construct P×G×C qubits according to determined algorithm operating parameters, wherein the algorithm operating parameters include a population size P, a chromosome size C, a gene size G, a rotation angle θ, a mutation probability, and a termination parameter, wherein the population size P is the number of phase-coded radar signals transmitted by the radar system, the gene size G is the length of the phase-coded radar signal transmitted by the radar system, and P, C, G, and θ are all greater than 0;

[0042] A first chromosome determination module is configured to determine gene sequences of P×C first chromosomes based on all the qubits, where each population includes C first chromosomes and each first chromosome includes G qubits;

[0043] a termination condition judgment module, configured to judge whether there is a first chromosome among all the first chromosomes that satisfies a preset termination condition; wherein the termination condition is set based on the termination parameter;

[0044] a termination module, configured to, if yes, use the first chromosome that meets a preset termination condition as the phase-coded radar signal sent by the radar system;

[0045] A second chromosome determination module is configured to, if not, update all the first chromosomes according to the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and the rotation angle θ to obtain P×C second chromosomes, each of the populations including C second chromosomes, and each of the second chromosomes including G quantum bits;

[0046] A loop module is used to replace all the first chromosomes in each population with all the second chromosomes, and enter the step of determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes.

[0047] The present invention also provides a signal determination system based on quantum genetic algorithm, comprising:

[0048] A quantum computing device, configured to execute the steps of the signal determination method based on quantum genetic algorithm as described above;

[0049] A radar system is configured to transmit a phase-coded radar signal determined according to the quantum computing device and receive an echo corresponding to the phase-coded radar signal.

[0050] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a signal determination system, the steps of the signal determination method based on quantum genetic algorithm as described above are implemented.

[0051] The present invention provides a signal determination method, a quantum computing device, and a medium based on a quantum genetic algorithm, which determine the number and length of phase-coded radar signals emitted by a radar system; construct P×G×C quantum bits according to determined algorithm operating parameters, where the algorithm operating parameters include a population size P, a chromosome size C, a gene size G, a rotation angle θ, a mutation probability, and a termination parameter, where the population size P is the number of phase-coded radar signals emitted by the radar system, the gene size G is the length of the phase-coded radar signal emitted by the radar system, and P, C, G, and θ are all greater than 0; determine the gene sequences of P×C first chromosomes according to all the quantum bits, where each population includes C first chromosomes, and each first chromosome includes G quantum bits; and determine whether there is a first chromosome among all the first chromosomes that meets a preset termination condition; wherein the termination condition can be set based on the termination parameter. If a first chromosome that meets a preset termination condition exists among all the first chromosomes, the first chromosome that meets the preset termination condition is used as the phase-coded radar signal transmitted by the radar system. If a first chromosome that meets the preset termination condition does not exist among all the first chromosomes, all the first chromosomes are updated according to the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle θ to obtain P×C second chromosomes, with each population containing C second chromosomes, and each second chromosome containing G quantum bits. All the first chromosomes in each population are replaced with all the second chromosomes, and the process proceeds to determine whether a first chromosome that meets the preset termination condition exists among all the first chromosomes. In this technical solution, quantum bits are constructed using the population size, chromosome number, and gene number, enriching chromosome diversity. A quantum rotation gate constructed using the rotation angle serves as the executor of population evolution, and its parallel computing characteristics can expand the search space and improve convergence speed. Chromosome mutation is achieved based on the mutation probability, the preset crossover strategy, the quantum catastrophe strategy, and the rotation angle, escaping local optimal solutions and improving search quality. Therefore, for the same population size, convergence speed is accelerated, improving the search efficiency of phase-coded radar signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the prior art and the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] FIG1 is a flow chart of a signal determination method based on a quantum genetic algorithm provided by an embodiment of the present invention;

[0054] FIG2 is a table of algorithm operation parameters provided by an embodiment of the present invention;

[0055] FIG3 is a diagram of a classical-quantum hybrid computing system provided by an embodiment of the present invention;

[0056] FIG4 is a table showing test data settings for algorithm operating parameters according to an embodiment of the present invention;

[0057] FIG5 is a diagram showing the results of obtaining a binary phase coded radar signal based on a quantum genetic algorithm according to an embodiment of the present invention;

[0058] FIG6 is another result diagram of obtaining a binary phase coded radar signal based on a quantum genetic algorithm provided by an embodiment of the present invention;

[0059] FIG7 is a schematic diagram of the structure of a quantum computing device based on a quantum genetic algorithm according to an embodiment of the present invention;

[0060] FIG8 is a schematic structural diagram of a signal determination system based on a quantum genetic algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The core of the present invention is to provide a signal determination method based on quantum genetic algorithm, which improves the search speed, saves resources, and converges faster when the population size is the same, thereby improving the search efficiency of phase-coded radar signals.

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0063] Please refer to FIG1 , which is a flowchart of a signal determination method based on a quantum genetic algorithm provided by an embodiment of the present invention. The method is applied to a signal determination system connected to a radar system. The determination method includes:

[0064] S100: Determine the number of phase-coded radar signals transmitted by the radar system and the length of the phase-coded radar signals transmitted by the radar system.

[0065] S101: Construct P×G×C quantum bits according to the determined algorithm operating parameters. The algorithm operating parameters include the population size P, the chromosome number C, the gene number G, the rotation angle θ, the mutation probability and the termination parameter. The population size P is the number of phase-coded radar signals emitted by the radar system, and the gene number G is the length of the phase-coded radar signal emitted by the radar system. P, C, G, and θ are all greater than 0.

[0066] In a specific embodiment, before using a quantum genetic algorithm to determine a phase-coded radar signal, it is necessary to first determine the algorithm operating parameters that affect many aspects of the quantum genetic algorithm, such as accuracy, reliability, and efficiency, as well as the quality of search results and system performance. That is, by analyzing the characteristics of a phase-coded radar signal with good characteristics, the population size P, the number of chromosomes C, the number of genes G, the rotation angle θ, the mutation probability, and the termination parameter are set. Among them, a larger rotation angle can expand the search space of the quantum genetic algorithm, but may miss the optimal solution and cause premature landing; a rotation angle that is too small will slow down the speed of generating new individuals, so the rotation angle should not be too small; a larger mutation probability will make the algorithm tend to be a purely random algorithm, while a value that is too small may cause the search to fall into a local solution; the population size and the maximum number of iterations have little impact; based on the above analysis and reference to relevant literature, the parameters are comprehensively designed. Please refer to Figure 2, which is an algorithm operating parameter table provided in an embodiment of the present invention.

[0067] S102: Determine the gene sequences of P×C first chromosomes based on all quantum bits, where each population includes C first chromosomes, and each first chromosome includes G quantum bits.

[0068] S103: Determine whether there is a first chromosome that meets a preset termination condition among all first chromosomes.

[0069] S104: If yes, use the first chromosome that meets the preset termination condition as the phase-coded radar signal sent by the radar system.

[0070] S105: If not, all first chromosomes are updated according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy and the rotation angle θ to obtain P×C second chromosomes, each population includes C second chromosomes, and each second chromosome includes G quantum bits.

[0071] S106: Replace all first chromosomes in each population with all second chromosomes, and proceed to the step of determining whether there is a first chromosome that meets a preset termination condition among all first chromosomes.

[0072] In a specific embodiment, to find the optimal phase-coded radar signal, after determining the gene sequences of all first chromosomes, a determination is made based on a preset termination condition to determine whether any of the first chromosomes meets the termination condition. The termination condition can be set based on a termination parameter. The termination parameter can include a fitness threshold or a preset iteration threshold.

[0073] It should be noted that this embodiment does not limit the specific content of the termination condition. Taking the termination parameter including the fitness threshold as an example, the fitness of all first chromosomes can be calculated through the fitness relationship. If there is a first chromosome whose fitness is greater than the preset fitness threshold, it is determined that there is a first chromosome that meets the termination condition.

[0074] For example, if the termination parameter includes a preset iteration threshold, the number of iterations corresponding to the first chromosome can be determined. If the number of iterations reaches the preset iteration threshold, it is determined that the first chromosome meets the termination condition. Alternatively, other termination conditions may be used, and should be selected by those skilled in the art based on actual circumstances.

[0075] If there is no first chromosome that meets the termination condition, based on the mutation probability, the preset crossover strategy, the quantum catastrophe strategy and the rotation angle θ, the individuals in another population perform genetic iteration of the first chromosome to obtain P×C second chromosomes. After recording the information of the first chromosomes and the second chromosomes, all the first chromosomes in each population are replaced with all the second chromosomes, and the judgment is made again whether there is a first chromosome that meets the preset termination condition among all the first chromosomes, until a first chromosome that meets the preset termination condition is found, and the cycle ends.

[0076] The present invention provides a signal determination method, a quantum computing device, and a medium based on a quantum genetic algorithm, which determine the number and length of phase-coded radar signals emitted by a radar system; construct P×G×C quantum bits according to determined algorithm operating parameters, where the algorithm operating parameters include a population size P, a chromosome size C, a gene size G, a rotation angle θ, a mutation probability, and a termination parameter, where the population size P is the number of phase-coded radar signals emitted by the radar system, the gene size G is the length of the phase-coded radar signal emitted by the radar system, and P, C, G, and θ are all greater than 0; determine the gene sequences of P×C first chromosomes according to all the quantum bits, where each population includes C first chromosomes, and each first chromosome includes G quantum bits; and determine whether there is a first chromosome among all the first chromosomes that meets a preset termination condition; wherein the termination condition can be set based on the termination parameter. If a first chromosome that meets a preset termination condition exists among all the first chromosomes, the first chromosome that meets the preset termination condition is used as the phase-coded radar signal transmitted by the radar system. If a first chromosome that meets the preset termination condition does not exist among all the first chromosomes, all the first chromosomes are updated according to the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle θ to obtain P×C second chromosomes, with each population containing C second chromosomes, and each second chromosome containing G quantum bits. All the first chromosomes in each population are replaced with all the second chromosomes, and the process proceeds to determine whether a first chromosome that meets the preset termination condition exists among all the first chromosomes. In this technical solution, quantum bits are constructed using the population size, chromosome number, and gene number, enriching chromosome diversity. A quantum rotation gate constructed using the rotation angle serves as the executor of population evolution, and its parallel computing characteristics can expand the search space and improve convergence speed. Chromosome mutation is achieved based on the mutation probability, the preset crossover strategy, the quantum catastrophe strategy, and the rotation angle, escaping local optimal solutions and improving search quality. Therefore, for the same population size, convergence speed is accelerated, improving the search efficiency of phase-coded radar signals.

[0077] Based on the above embodiment:

[0078] In some embodiments, when the phase-coded radar signal is a binary-phase-coded radar signal, constructing P×G×C quantum bits according to the determined algorithm operating parameters includes:

[0079] Construct P×G×C quantum bits in a uniform superposition state according to the determined algorithm operating parameters;

[0080] In a specific embodiment, after the algorithm operation parameters are determined, P×G×C qubits are constructed at the hardware level, and an H gate is applied to each qubit so that each qubit is in a uniform superposition state, which can be specifically expressed as:

[0081] Among them, any quantum bit |φ> is expressed as |φ>=α|0>+β|1>, and the two assigned constants α and β satisfy α 2 +β 2 =1; when measuring, the quantum bit in the uniform superposition state is α 2 The probability of collapse to the |0> state is β 2 The probability of collapse to the |1> state.

[0082] Determine the gene sequences of P×C first chromosomes based on all quantum bits, including:

[0083] Determine the classical bit state corresponding to the collapse of all quantum bits, the value of the classical bit state is 0 or 1;

[0084] P×C first chromosomes are determined according to all classical bit states, where the first chromosome is a binary sequence including G classical bit states.

[0085] In a specific embodiment, it is defined that each individual has 1 chromosome, each chromosome contains G quantum bits, and after measurement, each quantum bit collapses to a 0 or 1 classical bit state with a probability of 1 / 2 to obtain a set of determined individual states in is the state of the jth individual in the population, which is specifically expressed as a binary sequence of length G (x1, x2, ..., x G ), x i The value is 0 or 1.

[0086] Please refer to Figure 3, which is a diagram of a classical-quantum hybrid computing system provided by an embodiment of the present invention: the classical quantum hybrid computing system consists of two parts: quantum hardware 301 and classical processor 305, wherein the quantum hardware part includes a quantum system 302, a control device 303 and a readout device 304, and the relationship between the devices is as follows: Quantum system 302: Quantum system 302 includes a quantum chip and a quantum memory, which are used to store and process quantum information and realize the generation, operation and measurement of quantum states; Control device 303: Control device 303 is used to control the operation of the quantum system, including regulating the voltage for generating quantum states, adjusting the time sequence, adjusting the transition between quantum states, etc.; Readout device 304: Readout device 304 is used to measure and record information of the quantum system, including measuring quantum states, converting them into classical electrical signals, and sending them to the classical processor for processing; Classical processor 305: The classical processor 305 is used in conjunction with the quantum system, including a controller and signal processing, etc., to realize the programming, optimization and simulation of improved quantum genetic algorithms and other quantum algorithms.

[0087] The information exchanged between the quantum hardware 301 and the classical processor 305 includes:

[0088] 1) Quantum state information: The quantum state in the chip is read out by a readout device and processed in parallel;

[0089] 2) Control signal: The classical processor controls the quantum system through control signals;

[0090] 3) Reading data: The data is converted into a classical electrical signal by a readout device and sent to a classical processor for processing;

[0091] 4) Metadata: used to configure the quantum computing environment and monitor the status of the quantum computing process;

[0092] 5) Physical parameter information: Classic processors obtain corresponding physical parameter information and perform optimization feedback for more efficient control and calculation.

[0093] This embodiment forms a binary sequence of length G by using the classical bit state corresponding to the collapse of the quantum bit in the uniform superposition state, thereby determining the genetic sequence of the chromosome and obtaining discrete signal coding. Compared with the particle swarm algorithm and quantum particle swarm algorithm in the prior art, this embodiment overcomes the local optimal solution and can be applied to discrete problems without the need for more quantum gates and auxiliary quantum bits, thus having lower hardware requirements and saving hardware resources.

[0094] In some embodiments, after determining C first chromosomes according to all classical bit states, the method further includes:

[0095] All collapsed quantum bits are restored to their pre-collapse states based on all classical bit states.

[0096] In this embodiment, after a quantum measurement is performed, that is, after the quantum bit collapses to a classical bit state, in order to avoid the quantum bit from losing phase information after the measurement and thus maintain its superposition state before the measurement, an identical set of quantum gate operations is performed within each time step; continuous measurement is performed, and the measurement results of the quantum bit are recorded at the end of each time step; based on the measurement results, feedback control is performed and the state of the quantum bit is corrected, that is, the measurement results are processed to reconstruct the state of the quantum bit and use it for subsequent calculations and storage, and the above operations are repeated until the required accuracy is achieved.

[0097] In some embodiments, all first chromosomes are updated according to the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle θ to obtain P×C second chromosomes, including:

[0098] Determine m chromosomes to be mutated among all first chromosomes according to the mutation probability, where m is a positive integer not greater than C;

[0099] Apply the preset X gate to all chromosomes to be mutated to obtain m third chromosomes. The X gate is:

[0100] According to the preset crossover strategy, quantum catastrophe strategy, and rotation angle θ, the Cm first chromosomes and m third chromosomes are updated to obtain P×C second chromosomes. In practical applications, the value of P can be set to 1.

[0101] In this embodiment, the qubit of each individual in the population is mutated through the X-gate, that is, the probability of the qubit collapsing to 0 or 1 is flipped through the X-gate, which fully demonstrates the advantages of quantum computing and provides a theoretical basis for the construction of quantum computers.

[0102] In some embodiments, the interleaving strategy is a full-interference interleaving strategy.

[0103] In this embodiment, a full interference crossover strategy is used to randomly sort all individuals in the population, and the i-th position of all sorted individuals is cyclically shifted i-1 times to obtain a new population after the crossover operation, so that the information between each quantum chromosome is fully utilized and the probability of the occurrence of a local optimal solution is reduced.

[0104] In some embodiments, Cm first chromosomes and m third chromosomes are updated according to a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle θ to obtain P×C second chromosomes, including:

[0105] Merge Cm first chromosomes and m third chromosomes as a new first chromosome;

[0106] Determining fitness values ​​of all new first chromosomes according to all new first chromosomes and a preset fitness relationship expression;

[0107] All new first chromosomes are updated according to the rotation angle θ, the target chromosome with the largest fitness value in the previous iteration, and the preset quantum rotating gate expression to obtain P×C fourth chromosomes. The quantum rotating gate expression is:

[0108] Among them, when the i-th classical bit state of any chromosome in the new first chromosome is the same as the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, then θ i = 0; when the i-th classical bit state of any chromosome in the new first chromosome is different from the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, the rotation direction is determined based on the quadrant in which any chromosome in the new first chromosome is located, and θ is adjusted according to the rotation direction and the set adjustment angle. i .

[0109] For example, assuming that the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration is 0, if the i-th classical bit state of any chromosome in the new first chromosome is 0, and the two are the same, then the θ of any chromosome in the new first chromosome is i =0.

[0110] The probability amplitude can be expressed based on cos(θ) and sin(θ). When the value of cos(θ) is greater than the value of sin(θ), the classical bit state is 0; when the value of cos(θ) is less than the value of sin(θ), the classical bit state is 1. In practical applications, the x-axis can be used as the classical bit state 0 and the y-axis as the classical bit state 1. Combined with the quadrant in which any chromosome is located, the rotation direction of any chromosome can be determined. The rotation direction can include clockwise or counterclockwise rotation.

[0111] For example, assuming that the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration is 0, if the i-th classical bit state of any chromosome in the new first chromosome is 1, the two are different. At this time, the quadrant in which the chromosome is located can be determined based on the rotation angle of the arbitrary chromosome, and its rotation direction can be determined. Assuming that the arbitrary chromosome is in the first quadrant, in order to make it closer to the target chromosome with the largest fitness value, the rotation direction should be clockwise. Assuming that the arbitrary chromosome is in the second quadrant, in order to make it closer to the target chromosome with the largest fitness value, the rotation direction should be counterclockwise. The adjustment angle can be selected from the range of values ​​of the rotation angle in Figure 2.

[0112] Based on the chromosome with the largest fitness value among all fourth chromosomes and the target chromosome with the largest fitness value in the previous iteration, the target chromosome with the largest fitness value in the next iteration is determined.

[0113] If the fitness value of the chromosome with the largest fitness value among all fourth chromosomes is greater than the fitness value of the target chromosome with the largest fitness value in the previous iteration, the chromosome with the largest fitness value among all fourth chromosomes will be used as the target chromosome with the largest fitness value in the next iteration.

[0114] If the fitness value of the chromosome with the largest fitness value among all fourth chromosomes is less than or equal to the fitness value of the target chromosome with the largest fitness value in the previous iteration, the target chromosome with the largest fitness value in the previous iteration will be used as the target chromosome with the largest fitness value in the next iteration.

[0115] Determine whether the target chromosome with the largest fitness value has not changed for a set number of consecutive times; wherein the set number of times is determined based on the total number of iterations and the set proportion.

[0116] When the target chromosome with the largest fitness value does not change for the set number of consecutive times, the remaining chromosomes of all fourth chromosomes except the chromosome with the largest fitness value are initialized according to the set replacement ratio to obtain the second chromosome;

[0117] When the target chromosome with the largest fitness value is changed for the consecutive number of times, the fourth chromosome is used as the second chromosome.

[0118] In a specific embodiment, based on the quantum rotating gate update strategy, the best individual of the previous generation is selected as the evolution target, that is, the first chromosome with the highest fitness value is selected. The fitness relationship is:

[0119] Among them, X is the first chromosome of X, x i is the i-th classical bit state in the X-th first chromosome, x i+τ is the i+τth classical bit state in the Xth first chromosome; when brought into the fitness relationship, the classical bit state 0 is replaced by -1, and 1 remains unchanged to obtain the fitness function value.

[0120] A quantum revolving door is constructed according to the set rotation angle θ, so that the individuals in the population rotate closer to the target individual. The update process is:

[0121] The final result is:

[0122] Among them, (α i , β i )T and (α′ i , β′ i ) T Represents the probability amplitude of the individual's i-th quantum bit before and after being updated by the rotating gate.

[0123] In some embodiments, the individual with the largest fitness value can be considered the optimal individual. After determining the optimal individual in the current generation's population, the fitness of the optimal individual is compared with the fitness of the optimal individual in the previous generation. If the fitness of the optimal individual in the current generation's population is greater than the fitness of the optimal individual in the previous generation, the fitness of the optimal individual in the current generation's population is used as the target value for the next generation's quantum revolving gate. If the fitness of the optimal individual in the previous generation is less than the fitness of the optimal individual in the previous generation, the fitness of the optimal individual in the previous generation is used as the target value for the next generation's quantum revolving gate.

[0124] Based on this, if the fitness of the optimal individual remains constant over a certain number of generations during evolution, meaning no individuals with higher fitness emerge, the algorithm is considered to have fallen into a local optimum. At this point, a quantum catastrophe is necessary to minimize the local optimum. Specifically, the current optimal individual and a random subset of individuals in the current population are preserved, and the remaining individuals are replaced with the initial individuals, forming a new population to continue evolution. Generally, the number of generations that pass through a certain number of generations is one-tenth of the maximum number of evolutionary generations, and the random subset of individuals in the current population is a random two-fifths of the individuals in the current population.

[0125] For example, if the total number of iterations is 100, the set percentage is 1 / 10, and the replacement ratio is 3 / 5, then if the target chromosome with the highest fitness value does not change for 100*1 / 10=10 consecutive times, the chromosome with the highest fitness value among all current chromosomes and 2 / 5 of the chromosomes randomly selected from the remaining chromosomes can be saved, and all unsaved chromosomes are initialized, thereby merging the saved chromosomes and the initialized chromosomes as the second chromosome. This embodiment uses a quantum rotating gate to ensure that the corresponding quantum bits collapse to the optimal individual state with a greater probability after measurement, thereby accelerating convergence and improving efficiency.

[0126] In some embodiments, when the termination parameter includes a fitness threshold, determining whether there is a first chromosome among all first chromosomes that meets a preset termination condition includes:

[0127] Determining the fitness values ​​of all the first chromosomes according to all the first chromosomes and a preset fitness relationship expression;

[0128] Determine whether there is a first chromosome whose fitness value is not less than the fitness threshold among all first chromosomes;

[0129] If so, determining whether there is a first chromosome that meets the preset termination condition among all first chromosomes;

[0130] If not, it is determined that there is no first chromosome that meets the preset termination condition among all the first chromosomes.

[0131] In some embodiments, when the termination parameter includes a preset iteration threshold, determining whether there is a first chromosome that meets the preset termination condition among all first chromosomes includes:

[0132] Determine whether the current number of iterations of all first chromosomes reaches a preset iteration threshold;

[0133] If so, determining whether there is a first chromosome that meets the preset termination condition among all first chromosomes;

[0134] If not, it is determined that there is no first chromosome that meets the preset termination condition among all the first chromosomes.

[0135] This embodiment uses whether the fitness of the first chromosome reaches the fitness threshold or whether the number of iterations reaches the iteration threshold as a preset termination condition to ensure that the population gradually converges to the optimal solution as iterations proceed. For details, please refer to Figures 4, 5, and 6. Figure 4 is a table showing experimental data settings for algorithm operating parameters according to an embodiment of the present invention. Figure 5 is a graph showing the results of acquiring a binary phase-coded radar signal based on a quantum genetic algorithm according to an embodiment of the present invention. Figure 6 is another graph showing the results of acquiring a binary phase-coded radar signal based on a quantum genetic algorithm according to an embodiment of the present invention.

[0136] After repeated experiments and reference to relevant literature, and after comprehensively designing key operating parameters, three data sets were selected for experiments with signal lengths of 40, 50, and 60, respectively. Figure 5 shows that, while ensuring appropriate parameter selection, the average fitness of the population continues to increase with the number of evolutions as the number of genes, and therefore the length of the binary coded radar signal, increases. At signal lengths of 50 and 60, the number of evolutionary generations is around 5, and the average fitness decreases. However, as the population evolves, the average fitness increases again, demonstrating the crucial role of full-interference crossover, mutation operator, and catastrophe strategy in population evolution. Figure 6 shows the relationship between the optimal individual fitness and the number of evolutionary generations. The comparative experimental results show that the optimal individual fitness is optimized to varying degrees, with overall fitness remaining high. This further demonstrates that the acquired binary coded radar signal has low aperiodic autocorrelation function sidelobe peaks. Similarly, the optimization effect can be further improved by adjusting the mutation probability and rotation angle.

[0137] Please refer to FIG7 , which is a schematic diagram of the structure of a quantum computing device based on a quantum genetic algorithm according to an embodiment of the present invention. The device is applied to a signal determination system for a phase-coded radar signal. The signal determination system is connected to the radar system. The quantum computing device includes:

[0138] A qubit construction module 701 is configured to construct P×G×C qubits according to determined algorithm operation parameters, where the algorithm operation parameters include a population size P, a chromosome size C, a gene size G, a rotation angle θ, a mutation probability, and a termination parameter. The population size P is the number of phase-coded radar signals transmitted by the radar system, and the gene size G is the length of the phase-coded radar signal transmitted by the radar system. P, C, G, and θ are all greater than 0.

[0139] A first chromosome determination module 702 is configured to determine the gene sequences of P×C first chromosomes based on all qubits, where each population includes C first chromosomes and each first chromosome includes G qubits;

[0140] The termination condition judgment module 703 is used to judge whether there is a first chromosome that meets a preset termination condition among all first chromosomes; wherein the termination condition is set based on the termination parameter;

[0141] a termination module 704, configured to, if yes, use the first chromosome that meets the preset termination condition as the phase-coded radar signal sent by the radar system;

[0142] The second chromosome determination module 705 is configured to, if not, update all first chromosomes according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy, and the rotation angle θ to obtain P×C second chromosomes, where each population includes C second chromosomes, and each second chromosome includes G qubits;

[0143] The loop module 706 is used to replace all first chromosomes in each population with all second chromosomes, and enter the step of determining whether there is a first chromosome that meets a preset termination condition among all first chromosomes.

[0144] In some embodiments, when the phase-coded radar signal is a binary-phase-coded radar signal, the quantum bit building module includes:

[0145] A uniform superposition state construction module is used to construct P×G×C quantum bits in a uniform superposition state according to the determined algorithm operation parameters;

[0146] The first chromosome determination module includes:

[0147] The classical bit state building module is used to determine the classical bit state corresponding to the collapse of all quantum bits. The value of the classical bit state is 0 or 1.

[0148] P×C first chromosomes are determined according to all classical bit states, where the first chromosome is a binary sequence including G classical bit states.

[0149] In some embodiments, further comprising:

[0150] The quantum bit recovery module is used to restore all collapsed quantum bits to their pre-collapse states based on all classical bit states.

[0151] In some embodiments, the second chromosome determination module comprises:

[0152] A chromosome to be mutated determination module is used to determine m chromosomes to be mutated among all first chromosomes according to the mutation probability, where m is a positive integer not greater than C;

[0153] The X-gate module is used to apply the preset X-gate to all chromosomes to be mutated to obtain m third chromosomes. The X-gate is:

[0154] The first mutation module is used to update Cm first chromosomes and m third chromosomes according to a preset crossover strategy, quantum catastrophe strategy and rotation angle θ to obtain P×C second chromosomes.

[0155] In some embodiments, the interleaving strategy is a full-interference interleaving strategy.

[0156] In some embodiments, the first mutation module is configured to merge the Cm first chromosomes and the m third chromosomes into a new first chromosome;

[0157] Determining the fitness values ​​of all the new first chromosomes according to all the new first chromosomes and a preset fitness relationship expression;

[0158] All the new first chromosomes are updated according to the rotation angle θ, the target chromosome with the largest fitness value in the previous iteration, and a preset quantum rotating gate expression to obtain P×C fourth chromosomes. The quantum rotating gate expression is:

[0159] Among them, when the i-th classical bit state of any chromosome in the new first chromosome is the same as the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, then θ i = 0; when the i-th classical bit state of any chromosome in the new first chromosome is different from the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, the rotation direction is determined based on the quadrant in which any chromosome in the new first chromosome is located, and θ is adjusted according to the rotation direction and the set adjustment angle. i ;

[0160] Determining a target chromosome with the largest fitness value in the next iteration based on the chromosome with the largest fitness value among all the fourth chromosomes and the target chromosome with the largest fitness value in the previous iteration;

[0161] Determine whether the target chromosome with the largest fitness value has not changed for a set number of consecutive times; wherein the set number of times is determined based on the total number of iterations and the set proportion;

[0162] When the target chromosome with the largest fitness value does not change for a set number of consecutive times, the remaining chromosomes of all the fourth chromosomes except the chromosome with the largest fitness value are initialized according to the set replacement ratio to obtain the second chromosome;

[0163] When the target chromosome with the largest fitness value is set to change for a consecutive number of times, the fourth chromosome is used as the second chromosome.

[0164] In some embodiments, when the termination parameter includes a fitness threshold, the termination condition determination module is configured to:

[0165] A second fitness determination module is used to determine the fitness values ​​of all the first chromosomes according to all the first chromosomes and a preset fitness relationship formula;

[0166] A fitness judgment module is used to judge whether there is a first chromosome whose fitness value is not less than a fitness threshold among all first chromosomes;

[0167] a condition satisfaction module, configured to determine whether there is a first chromosome that satisfies a preset termination condition among all first chromosomes;

[0168] The condition not satisfied module is used to determine if no first chromosome exists in all first chromosomes that satisfies the preset termination condition.

[0169] In some embodiments, when the termination parameter includes a preset iteration threshold, the termination condition determination module is configured to:

[0170] Determine whether the current number of iterations of all first chromosomes reaches a preset iteration threshold;

[0171] If so, determining whether there is a first chromosome that meets the preset termination condition among all first chromosomes;

[0172] If not, it is determined that there is no first chromosome that meets the preset termination condition among all the first chromosomes.

[0173] Please refer to FIG8 , which is a schematic diagram of the structure of a signal determination system based on a quantum genetic algorithm provided by an embodiment of the present invention, including:

[0174] A quantum computing device 801 is configured to execute the steps of the above-mentioned signal determination method based on quantum genetic algorithm;

[0175] The radar system 802 is configured to transmit a phase-coded radar signal determined by the quantum computing device 801 and receive an echo corresponding to the phase-coded radar signal.

[0176] When the phase-coded radar signal with excellent aperiodic characteristics output by quantum computing device 801 is used as input to the waveform generator of radar system 802, the waveform generator generates a corresponding baseband signal. Radar system 802 primarily consists of an antenna, a transmitter, a waveform generator, a transmitter, a receiver, a signal processor, a data processor, and a terminal.

[0177] The functions of each part are briefly summarized as follows:

[0178] (1) Antenna. Radiates high-power signals and receives target scattered echo signals;

[0179] (2) Waveform generator, also known as frequency synthesizer (abbreviated as frequency synthesizer). Generates RF excitation signal and provides coherent local oscillator signal to radar receiver;

[0180] (3) Transmitter, i.e., the high-power transmitting part. It amplifies and filters the RF excitation signal;

[0181] (4) Receiver, i.e. low-power receiving part. It amplifies, mixes, filters, etc. the received signal;

[0182] (5) Signal processor. It processes the echo signal accordingly to improve the signal-to-noise ratio of the target echo, suppress clutter and interference, and detect the target;

[0183] (6) Data processor. Performs track management and tracking filtering on the detection results;

[0184] (7) Terminal display and data transmission. Display the original video and trace of the echo signal, and upload the trace information of the target.

[0185] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a signal determination system, the steps of the signal determination method based on quantum genetic algorithm as described above are implemented.

[0186] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0187] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0188] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Claims

1. A signal determination method based on quantum genetic algorithm, characterized in that: Applied to a signal determination system, the signal determination system is connected to a radar system, and the determination method includes: determining a number of phase coded radar signals transmitted by the radar system and a length of the phase coded radar signals transmitted by the radar system; Constructing P×G×C quantum bits according to the determined algorithm operation parameters, wherein the algorithm operation parameters include the population size P, the chromosome number C, the gene number G, the rotation angle θ, the mutation probability and the termination parameter, the population size P is the number of phase-coded radar signals emitted by the radar system, the gene number G is the length of the phase-coded radar signal emitted by the radar system, and P, C, G, and θ are all greater than 0; Determine the gene sequences of P×C first chromosomes according to all the qubits, each population includes C first chromosomes, and each first chromosome includes G qubits; Determine whether there is a first chromosome that meets a preset termination condition among all the first chromosomes; wherein the termination condition is set based on the termination parameter; If so, using the first chromosome that meets the preset termination condition as the phase-coded radar signal sent by the radar system; If not, all the first chromosomes are updated according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy and the rotation angle θ to obtain P×C second chromosomes, each of the populations includes C second chromosomes, and each of the second chromosomes includes G quantum bits; All the first chromosomes in each of the populations are replaced with all the second chromosomes, and the step of determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes is entered.

2. The signal determination method based on quantum genetic algorithm according to claim 1, characterized in that: When the phase-coded radar signal is a two-phase coded radar signal, constructing P×G×C quantum bits according to the determined algorithm operation parameters includes: Construct P×G×C quantum bits in a uniform superposition state according to the determined algorithm operation parameters; Determining the gene sequences of P×C first chromosomes according to all the quantum bits includes: Determine the classical bit state corresponding to all the quantum bits after collapse, the value of the classical bit state is 0 or 1; P×C first chromosomes are determined according to all the classical bit states, where the first chromosomes are binary sequences including G of the classical bit states.

3. The signal determination method based on quantum genetic algorithm as claimed in claim 2, characterized in that: After determining C first chromosomes according to all the classical bit states, the method further includes: All collapsed quantum bits are restored to their pre-collapse states based on all the classical bit states.

4. The signal determination method based on quantum genetic algorithm according to claim 2, characterized in that: All the first chromosomes are updated according to the mutation probability, the preset crossover strategy, the quantum catastrophe strategy and the rotation angle θ to obtain P×C second chromosomes, including: Determine m chromosomes to be mutated among all the first chromosomes according to the mutation probability, where m is a positive integer not greater than C; Apply the preset X gate to all the chromosomes to be mutated to obtain m third chromosomes. The X gate is: According to the preset crossover strategy, quantum catastrophe strategy and the rotation angle θ, Cm first chromosomes and m third chromosomes are updated to obtain P×C second chromosomes.

5. The signal determination method based on quantum genetic algorithm as claimed in claim 4, characterized in that: The crossover strategy is a full interference crossover strategy.

6. The signal determination method based on quantum genetic algorithm according to claim 4, characterized in that: According to the preset crossover strategy, quantum catastrophe strategy and the rotation angle θ, Cm first chromosomes and m third chromosomes are updated to obtain P×C second chromosomes, including: Merging Cm first chromosomes and m third chromosomes as a new first chromosome; Determining the fitness values ​​of all the new first chromosomes according to all the new first chromosomes and a preset fitness relationship formula; All the new first chromosomes are updated according to the rotation angle θ, the target chromosome with the largest fitness value in the previous iteration, and the preset quantum revolving gate expression to obtain P×C fourth chromosomes. The quantum revolving gate expression is: Among them, when the i-th classical bit state of any chromosome in the new first chromosome is the same as the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, then θ i =0; when the i-th classical bit state of any chromosome in the new first chromosome is different from the i-th classical bit state of the target chromosome with the largest fitness value in the previous iteration, the rotation direction is determined based on the quadrant in which any chromosome in the new first chromosome is located, and θ is adjusted according to the rotation direction and the set adjustment angle i ; Based on the chromosome with the largest fitness value among all the fourth chromosomes and the target chromosome with the largest fitness value in the previous iteration, determining the target chromosome with the largest fitness value in the next iteration; Determine whether the target chromosome with the largest fitness value has not changed for a set number of consecutive times; wherein the set number of times is determined based on the total number of iterations and the set proportion; When the target chromosome with the largest fitness value does not change for a set number of consecutive times, the remaining chromosomes of all the fourth chromosomes except the chromosome with the largest fitness value are initialized according to the set replacement ratio to obtain the second chromosome; When the number of times the target chromosome with the largest fitness value is set continuously changes, the fourth chromosome is used as the second chromosome.

7. The signal determination method based on quantum genetic algorithm according to any one of claims 1 to 6, characterized in that: When the termination parameter includes a fitness threshold, determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes includes: Determining the fitness values ​​of all the first chromosomes according to all the first chromosomes and a preset fitness relationship formula; Determine whether there is a first chromosome whose fitness value is not less than the fitness threshold among all the first chromosomes; If so, determining that there is a first chromosome that satisfies a preset termination condition among all the first chromosomes; If not, it is determined that there is no first chromosome satisfying a preset termination condition among all the first chromosomes.

8. A quantum computing device based on a quantum genetic algorithm, characterized in that: A signal determination system applied to a phase-coded radar signal, the signal determination system being connected to a radar system, the quantum computing device comprising: A quantum bit construction module, used to construct P×G×C quantum bits according to determined algorithm operation parameters, wherein the algorithm operation parameters include a population size P, a chromosome size C, a gene size G, a rotation angle θ, a mutation probability, and a termination parameter, wherein the population size P is the number of phase-coded radar signals emitted by the radar system, the gene size G is the length of the phase-coded radar signal emitted by the radar system, and P, C, G, and θ are all greater than 0; A first chromosome determination module, used to determine the gene sequences of P×C first chromosomes according to all the qubits, each population includes C first chromosomes, and each first chromosome includes G qubits; A termination condition judgment module, used to judge whether there is a first chromosome that meets a preset termination condition among all the first chromosomes; wherein the termination condition is set based on the termination parameter; a termination module, configured to use the first chromosome that meets a preset termination condition as the phase-coded radar signal sent by the radar system; A second chromosome determination module, for, if not, updating all the first chromosomes according to the mutation probability, a preset crossover strategy, a quantum catastrophe strategy and the rotation angle θ to obtain P×C second chromosomes, each of the populations including C second chromosomes, and each of the second chromosomes including G quantum bits; A loop module is used to replace all the first chromosomes in each of the populations with all the second chromosomes, and enter a step of determining whether there is a first chromosome that meets a preset termination condition among all the first chromosomes.

9. A signal determination system based on quantum genetic algorithm, characterized in that: include: A quantum computing device, used to perform the steps of the signal determination method based on quantum genetic algorithm as described in any one of claims 1 to 7; A radar system is used to transmit a phase-coded radar signal determined according to the quantum computing device and receive an echo corresponding to the phase-coded radar signal.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the signal determination system, the steps of the signal determination method based on quantum genetic algorithm according to any one of claims 1 to 7 are implemented.

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