Methods, apparatus, devices, storage media, and software products for optimizing the coupling of quantum parameters.

Abstract

This application relates to a method, apparatus, device, storage medium, and program product for coupling optimization of AC quantum parameters. The method includes: initializing an initial population to obtain an initialized first population; spatially mapping the individuals in the first population according to a preset coupling index model to obtain a second population; selecting individuals in the second population based on their non-dominance level and crowding distance to obtain a third population; performing crossover and mutation operations on the individuals in the third population to generate a fourth population; performing iterative optimization based on the fourth population, and outputting the second parameters of the optimal individuals obtained during the iterative optimization process after completion. This method can effectively improve the performance of AC quantum signals, thereby achieving efficient optimization of AC quantum signals.

Description

Technical Field

[0001] This application relates to the field of AC quantum voltage technology, and in particular to a method, apparatus, device, storage medium, and program product for optimizing the coupling of AC quantum parameters. Background Technology

[0002] The Josephson effect, a major discovery in condensed matter physics, possesses immense scientific significance and practical value. The AC Josephson effect generates quantized voltage steps through superconducting Josephson arrays driven by microwave sources, forming the core technology of quantum voltage standards. Quantum voltage standards, with their high precision and stability, play a crucial role in electrical metrology and precision instrument calibration. In the actual synthesis of superconducting Josephson arrays, a strong coupling relationship exists between the voltage step width W, the number of steps N, and the transition time tr. Precise control of these three parameters plays a decisive role in the performance of the Josephson array, directly affecting the accuracy and stability of the quantum voltage standard.

[0003] Traditional approaches typically employ a single-parameter successive adjustment method to handle the coupling relationship between these three elements. However, this method struggles to address the complex nonlinear relationships involving multi-parameter coupling, making the state of the entire AC quantum synthesis voltage system difficult to predict and control. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, device, storage medium, and program product for optimizing the coupling of quantum parameters to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for optimizing the coupling of quantum parameters, comprising:

[0006] The initial population is initialized to obtain the first population after initialization; the first population consists of N individuals, each representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time;

[0007] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group; each individual in the second group represents the second parameter of the AC quantum signal to be optimized; the second parameter includes voltage step width, number of steps, transition time, maximizing the effective value of AC voltage, and minimizing the transition time;

[0008] The coupling index model includes:

[0009] ;

[0010] in, Represents the time constant; Indicates the number of steps; Indicates a time period; Indicates the duration of the transition process; Indicates constant coefficients; Indicates the transition time;

[0011] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0012] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0013] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0014] In one embodiment, individuals in the second population are screened based on their non-dominance level and crowding distance to obtain a third population, which includes:

[0015] For each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined based on the comparison results.

[0016] Sort the individuals in the second population in descending order of non-dominance level, and extract a predetermined number of individuals from the sorted second population based on the crowding distance of each individual to obtain the third population.

[0017] In one embodiment, a predetermined number of individuals are extracted from the sorted second population based on the crowding distance to obtain a third population, including:

[0018] Select the top-ranked individuals from the second group after sorting as multiple candidate individuals;

[0019] If the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance;

[0020] Individuals with the highest crowding distance from multiple candidates are selected as individuals in the third group.

[0021] In one embodiment, the above-mentioned method for optimizing the coupling of the quantum parameters includes:

[0022] Compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0023] Crossover and mutation operations are performed on individuals in the third population to generate a fourth population, including:

[0024] Crossover and mutation operations are performed on individuals in the fifth population to generate the fourth population.

[0025] In one embodiment, the above-mentioned method for optimizing the coupling of the quantum parameters includes:

[0026] A diversity assessment was conducted on the first population, and the assessment results were obtained.

[0027] If the evaluation results indicate that the first population does not meet the preset diversity requirements, the initial population will be re-initialized.

[0028] If the evaluation results indicate that the first population meets the preset diversity requirements, then the step of spatially mapping each individual in the first population according to the preset coupling index model is executed to obtain the second population.

[0029] In one embodiment, the initial population is initialized, including:

[0030] Set the initial population size;

[0031] Based on the preset physical constraints, mathematical characteristics, and transition time conditions, the feasible domain range of the first parameter of each individual in the initial population is defined.

[0032] A uniform random sampling strategy or a Latin hypercube sampling algorithm is used to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region.

[0033] Secondly, this application also provides a coupling optimization device for exchanging quantum parameters, comprising:

[0034] The initialization module is used to initialize the initial population and obtain the first population after initialization.

[0035] The mapping module is used to spatially map each individual in the first group according to a preset coupling index model to obtain the second group.

[0036] The screening module is used to screen individuals in the second population based on their non-dominance level and crowding distance to obtain the third population.

[0037] The crossover module is used to perform crossover and mutation operations on individuals in the third population to generate the fourth population.

[0038] The iteration module is used to perform iterative optimization based on the fourth population and output the second parameter of the best individual in the iterative optimization process after the iterative optimization is completed.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0040] The initial population is initialized to obtain the first initialized population;

[0041] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group;

[0042] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0043] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0044] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0046] The initial population is initialized to obtain the first initialized population;

[0047] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group;

[0048] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0049] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0050] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0051] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0052] The initial population is initialized to obtain the first initialized population;

[0053] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group;

[0054] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0055] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0056] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0057] The aforementioned method, apparatus, device, storage medium, and program product for optimizing the coupling of quantum parameters of the AC signal obtains an initialized first population by initializing an initial population. The first population comprises N individuals, each representing a first parameter of the AC quantum signal to be optimized. The first parameter includes the voltage step width, the number of steps, and the transition time. Based on a preset coupling index model, spatial mapping is performed on each individual in the first population to obtain a second population. Each individual in the second population represents a second parameter of the AC quantum signal to be optimized. The second parameter includes the voltage step width, the number of steps, the transition time, maximizing the effective value of the AC voltage, and minimizing the transition time. Based on the non-dominance level and crowding distance of each individual in the second population, individual selection is performed to obtain a third population. The process is repeated for each individual in the second population. Individuals from the three populations undergo crossover and mutation operations to generate a fourth population. Iterative optimization is then performed based on the fourth population, and the second parameter of the optimal individual during the iterative optimization process is output after the optimization is complete. The above method provides a way to search for the optimal parameter combination of three input variables: voltage step width, number of steps, and transition time. By constructing a coupling index model, the coupling between voltage step width, number of steps, and transition time is optimally calculated, solving the problem of easily getting trapped in local optima in traditional algorithms. At the same time, the transition time is treated as an independent optimization objective, parallel to maximizing the effective voltage value. This ensures that while considering the coupling between voltage step width, number of steps, and transition time, the steady-state accuracy and dynamic response speed of the AC quantum synthesized voltage system can also be guaranteed. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a diagram illustrating the application environment of a coupling optimization method for quantum parameters in one embodiment.

[0060] Figure 2 This is one of the flowcharts illustrating the coupling optimization method for quantum parameters in one embodiment;

[0061] Figure 3 This is a second flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0062] Figure 4 This is the third flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0063] Figure 5 This is the fourth flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0064] Figure 6 This is the fifth flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0065] Figure 7 This is the sixth flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0066] Figure 8 This is the seventh flowchart illustrating the coupling optimization method for quantum parameters in one embodiment;

[0067] Figure 9 This is a structural block diagram of a coupling optimization device for exchanging quantum parameters in one embodiment. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0069] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0070] The coupling optimization method for AC quantum parameters provided in this application can be applied to, for example... Figure 1 The internal structure diagram of the computer device shown can be as follows: Figure 1 As shown.

[0071] The computer device includes a processor, memory, input / output interfaces, a communication interface, and input devices. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input devices are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a coupling optimization method for alternating quantum parameters.

[0072] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. A specific computing system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0073] In one exemplary embodiment, such as Figure 2 As shown, a coupling optimization method for alternating quantum parameters is provided, which is then applied to... Figure 1 Taking computer devices as an example, the explanation includes:

[0074] S201, initialize the initial population to obtain the first initialized population.

[0075] The first group consists of N individuals, each representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width. Number of steps and transition process time .

[0076] In this embodiment of the application, when generating quantum voltage, the computer device needs to take into account the voltage step width. Number of steps and transition process time The coupling relationship between them is then used to output the optimal solution set representing different performance trade-offs.

[0077] Regarding voltage step width Number of steps and transition process time The coupling relationship between these three elements is specifically reflected in the following two aspects:

[0078] Firstly, the proportional relationship between the number of steps and the width of the voltage step can be expressed by equation (1), which is shown below:

[0079] (1);

[0080] in, The frequency of the output AC quantum voltage signal; This refers to the voltage step width; This represents the number of steps.

[0081] Secondly, frequency Transition process time The coupling relationship, that is, the number of steps With the frequency unchanged When increased, the voltage step width The reduction leads to a shorter transition time. The proportion of [something] has increased.

[0082] In summary, the computer equipment needs to search the experimental data for information about the voltage step width. Number of steps and transition process time The optimal combination of these three input variables maximizes the effective value of the final output AC voltage. During the search, the computer employs a real-number encoding strategy, representing each combination of parameters to be optimized as a three-dimensional vector. These individuals are referred to as individuals. First, the computer device initializes the initial population based on the actual situation, determining the number of individuals in the initial population, i.e., the number of individuals in the first population. Then, based on three constraints—the physical constraints of the hardware system generating the quantum voltage, the coupling mathematical characteristics of the three input variables, and the transition time condition—the computer device defines the feasible domain of each individual in the first population, ensuring the rationality and feasibility of each parameter value. Next, the computer device generates parameter values ​​for each individual in the first population through random sampling. During the parameter value generation process, the computer device simultaneously introduces a boundary verification mechanism to ensure that the parameter values ​​of each individual satisfy the constraints. Finally, the computer device obtains the initialized first population and completes the parameter encoding.

[0083] S202, based on the preset coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group.

[0084] In this context, each individual in the second population represents a second parameter of the AC quantum signal to be optimized; the second parameter includes the voltage step width, the number of steps, the transition time, maximizing the effective value of the AC voltage, and minimizing the transition time.

[0085] In this embodiment of the application, the computer device is for each individual in the first group. The system invokes a pre-defined coupling index model to spatially map each individual value to a pre-defined comprehensive performance database, thereby obtaining two objective function values: maximizing the effective value of AC voltage. And minimize the transition time. The coupling index model can be expressed by relation (2), which is shown below:

[0086] (2);

[0087] in, Represents the time constant; Indicates the number of steps; Indicates a time period; Indicates the duration of the transition process; Indicates the transition time; This represents a constant coefficient, which can be 0.01.

[0088] In the process of modeling the coupling index model, the computer device firstly recognizes that the essence of the Josephson quantum voltage is a stepped wave formed by splicing together N discrete quantum steps within a period T. Therefore, the synthesis of the quantum voltage can be expressed by relation (3), which is shown below.

[0089] (3);

[0090] in, The step value representing a single quantum voltage; Indicates the number of Josephson knots; This represents the Josephson constant, with a value of 483597.9 GHz / V; Indicates microwave frequency; Indicates the period of the synthesized AC voltage; Indicates the current time.

[0091] Subsequently, the computer device analyzed the finite response speed of the Josephson array and concluded that the switching process between voltage steps was not an ideal instantaneous completion, nor a simple linear transition, but rather a dynamic process that approached the target value exponentially. Therefore, the computer device can establish a voltage time-domain model of an exponentially rising transition process, expressed by equation (4), which is shown below:

[0092] (4);

[0093] in, for The instantaneous value of the synthesized voltage at time t. and The first With the The amplitude of each quantum voltage step, Indicates the first The starting moment of each step, The exponential rise time constant, which characterizes the system's response speed, is set to 10 based on engineering experience. This refers to the duration of the transition process.

[0094] For the A step space, its voltage square integral It includes two parts: the transient process and the steady process, and the voltage square integral. It can be expressed by relation (5), which is shown below:

[0095] (5);

[0096] The expression of the voltage time-domain model Substituting into relation (5), we can obtain the voltage square integral. The expression is changed from relation (5) to relation (6), which is shown below:

[0097] (6);

[0098] in, Indicates the width of the step; It represents the voltage difference between adjacent voltage steps.

[0099] Taking advantage of the property that the synthesized voltage approximates an ideal sine wave, and using the method of continuous integration to approximate discrete summation, the sum of the voltage differences between adjacent steps can be expressed by the relation (7), which is shown below:

[0100] (7);

[0101] Substituting the derivative of the ideal sinusoidal voltage and performing integration, we obtain equation (8), which is shown below:

[0102] (8);

[0103] Combining equations (3) to (8), we can obtain that the voltage square integral over period T can be expressed by equation (9), which is shown below:

[0104] (9);

[0105] At the same time, according to the definition of effective value, relation (9) can be transformed into relation (10), which is shown below:

[0106] (10);

[0107] In relation (10), Usually very small, so It can be equivalent to Finally, the effective value of the synthesized voltage It can be expressed by relation (11), which is shown below:

[0108] (11);

[0109] From relation (11), it can be seen that the error generated by the Josephus array during the synthesis of quantum voltage is: This application primarily searches for information regarding voltage step width. Number of steps and transition process time The optimal combination of these three input variables maximizes the effective value of the final output AC voltage, thus minimizing the error. Defined as error factor, using To express, It can be expressed by relation (12), which is shown below:

[0110] (12);

[0111] In a quantum voltage synthesis system, hardware devices including Josephus arrays, microwave drive sources, current sources, and digital-to-analog converters vary in transition time due to the synchronicity of the digital-to-analog converters. The transition time fluctuates within a certain time period. Therefore, the computer device adds a penalty term P to relation (12), which can be expressed by relation (13), as shown below:

[0112] (13);

[0113] in, Indicates the transition time; This represents a constant coefficient, which can be 0.01.

[0114] By combining relation (12) and relation (13), the device can complete the modeling of the coupling index model, i.e., relation (2). The computer device performs spatial mapping between each individual in the first group and the preset comprehensive performance database according to the coupling index model, thereby obtaining two objective function values ​​and obtaining the second group.

[0115] S203. Based on the non-dominance level and crowding distance of each individual in the second population, individuals in the second population are screened to obtain the third population.

[0116] The non-dominated rank is obtained by the computer device using a non-dominated sorting mechanism to sort the individuals in the second population; the crowding distance is calculated by the computer device using a non-dominated sorting genetic algorithm II (NSGA-II) with an elitist strategy.

[0117] In this embodiment, the computer device first performs genetic operations (including selection, crossover, and mutation) on the second population based on the NSGA-II algorithm to generate a progeny population of size N. Then, the computer device merges this progeny population with the second population to obtain a merged population containing 2N individuals. For this merged population, the computer device uses a non-dominated sorting mechanism to perform hierarchical sorting. Taking individuals A and B as examples, individual A can be defined as dominating individual B if and only if individual A is not inferior to individual B in either of the two objective functions (maximizing the effective value of AC voltage and minimizing the transient process time), and is strictly superior to individual B in at least one objective function. Each individual in the merged population is checked to determine whether any individual B is dominated by other individuals. If any individual is not dominated by other individuals, then any individual B is added to the non-dominated layer of the fifth population, and the corresponding non-dominated level of individual B is determined. If the number of individuals after screening by the computer exceeds the capacity limit of the fifth population, NSGA-II is used to calculate the crowding distance of the individuals, thereby performing a second screening to ensure that the N individuals in the final fifth population have a good uniform distribution in the target space.

[0118] S204, perform crossover and mutation operations on individuals in the third population to generate the fourth population.

[0119] In this embodiment, after obtaining the third population, the computer device performs genetic operations on the individuals within the third population that use real-number encoding. Specifically, this includes crossover and mutation operations: the crossover operation employs a simulated binary crossover (SBX) strategy, probabilistically recombinating gene segments from two parent individuals to generate offspring individuals with parental characteristics; the mutation operation employs a multinomial mutation mechanism, randomly perturbing the gene values ​​of individuals with a low probability to maintain population diversity. Through these operations, the computer device ultimately obtains a fourth population of the same size as the third population.

[0120] S205 performs iterative optimization based on the fourth population and outputs the second parameter of the best individual in the iterative optimization process after the iterative optimization is completed.

[0121] In this embodiment, the computer device defines the multiple populations obtained after executing S201 to S204 as a complete evolutionary generation, and iterates and updates them sequentially in the main function using a for loop until a preset termination condition is met, such as reaching the maximum number of iterations or achieving the expected effective value of the synthesized AC voltage to meet the synthesized voltage requirement. Finally, after completing the iterative optimization, the computer device outputs the second parameters of the optimal individual in the iterative optimization, namely, the voltage step width, the number of steps, the transition time, the maximum target effective value of the target AC voltage, and the minimum target transition time.

[0122] In the above-mentioned coupling optimization method for AC quantum parameters, an initial population is initialized to obtain an initialized first population. The first population includes N individuals, each representing the first parameter of the AC quantum signal to be optimized. The first parameter includes the voltage step width, the number of steps, and the transition time. Based on a preset coupling index model, each individual in the first population is spatially mapped to obtain a second population. Each individual in the second population represents the second parameter of the AC quantum signal to be optimized. The second parameter includes the voltage step width, the number of steps, the transition time, maximizing the effective value of the AC voltage, and minimizing the transition time. Based on the non-dominated level and crowding distance of each individual in the second population, individuals in the second population are screened to obtain a third population. Crossover and mutation operations are performed on the individuals in the third population to generate a fourth population. Iterative optimization is performed based on the fourth population, and after completing the iterative optimization, the second parameter of the optimal individual in the iterative optimization process is output. The above method provides a way to search for the optimal combination of parameters for three input variables: voltage step width, number of steps, and transition time. By constructing a coupling index model, the coupling between voltage step width, number of steps, and transition time is optimally calculated, solving the problem of getting trapped in local optima in traditional algorithms. At the same time, the transition time is treated as an independent optimization objective, parallel to maximizing the effective voltage value. This ensures that while considering the coupling between voltage step width, number of steps, and transition time, the steady-state accuracy and dynamic response speed of the AC quantum synthesized voltage system can also be guaranteed.

[0123] In one exemplary embodiment, such as Figure 3 As shown, the phrase "based on the non-dominance level and crowding distance of each individual in the second population, perform individual screening in the second population to obtain the third population" in S203 above includes:

[0124] S301, for each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined based on the comparison results.

[0125] In this embodiment, the computer device merges the second population with the offspring population obtained through genetic operations, and performs hierarchical sorting on the merged second population using a non-dominated sorting mechanism. During the hierarchical sorting process, two objective functions are used (maximizing the effective value of AC voltage). Minimize the transition time Based on this, and if and only if A is not inferior to B in all objectives (i.e., ... _A≥ _B at the same time _A≤ _B) and is strictly superior to B (i.e., B) in at least one objective. _A> _B or _A< _B), denoted as individual A dominating B. Examples of individuals A, B, and C are shown in Table 1;

[0126] Table 1

[0127] individual Maximize the effective value of AC voltage (kV) Minimize the transition time (ns) Domination Relationship A 10 5 Dominate C B 8 4 No dominance relationship C 9 6 Dominated by A

[0128] The stratification process is iterated until all individuals in the second population are stratified and the non-dominant rank of each individual in the second population is obtained.

[0129] S302, sort the individuals in the second population in descending order of non-dominance level, and extract a predetermined number of individuals from the sorted second population based on the crowding distance of each individual to obtain the third population.

[0130] The preset number is the same as the number of individuals in the third group.

[0131] In this embodiment, the computer device performs a hierarchical sorting of the merged second population according to a non-dominated sorting mechanism. The hierarchical sorting process is iterative until all individuals are assigned to a layer F, and each layer contains a set of non-dominated individuals. For example, before starting the hierarchical sorting, the computer device first sets a level counter: Rank=1; then, it defines a set S = the merged second population; it iterates through each individual B in set S, checking whether individual B is dominated by any other individual in S. If no other individual dominates B, B is added to the current layer F1; then, all individuals in F1 are removed from set S; Rank=Rank+1, and the remaining individuals are iterated through again for non-dominance checks to find all individuals in the current S that are not dominated by any other individual, forming the next layer F2; this operation is repeated until set S is empty (i.e., all individuals have been assigned). When selecting individuals from the merged second population to enter the fifth population, the computer prioritizes individuals with high non-dominant ranks, starting from the Rank=1 layer (F1 layer). All individuals in the fifth population are selected. If the number of individuals in the fifth population does not reach the preset number, individuals from the Rank=2 layer (F2 layer) are selected. When a layer (e.g., Fn layer) is selected, and the number of individuals in that layer exceeds the remaining space in the fifth population, NSGA-II is used to calculate the crowding distance of individuals in Fn layer, extracting the preset number of individuals to finally obtain the third population.

[0132] In one exemplary embodiment, such as Figure 4As shown, the step S302 above, "extracting a preset number of individuals from the sorted second population based on the crowding distance to obtain a third population," includes:

[0133] S401, select the top-ranked individuals from the sorted second population as multiple candidate individuals.

[0134] In this embodiment, the computer device performs a non-dominated sorting operation on the individuals in the second population. The non-dominated sorting mechanism sorts all individuals in the second population, ultimately generating a hierarchical structure consisting of dominance layers {F1, F2, ..., Fn}, where F1 is the optimal dominance layer, F2 is the second-best dominance layer, and the dominance priority of subsequent layers decreases sequentially. Subsequently, the computer device filters candidate individuals according to the hierarchical priority from high to low. Starting with layer F1, all individuals in layer F1 are included in the candidate individual set. If the number of individuals in F1 does not meet the preset size of the third population, all individuals in F2 are added to the candidate individual set, and so on, until the number of candidate individuals reaches a preset threshold.

[0135] S402, if the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance.

[0136] In this embodiment, when the computer device performs hierarchical priority selection on multiple candidate individuals, upon selecting a certain layer Fn, it first calculates the total number of individuals in that layer after adding them to the current candidate set. If the total number exceeds a preset number, the computer device calculates the crowding distance for each candidate individual in the current layer. The crowding distance of each individual is used to measure the degree of isolation of the individual in the target space; a larger distance indicates fewer solutions around the individual. Subsequently, the computer device sorts the candidate individuals in the current layer according to their crowding distance from high to low, so that further screening or decision-making can be made based on the sorting results.

[0137] S403 selects individuals from multiple candidate individuals whose crowding distance ranks highest as individuals in the third population.

[0138] In this embodiment, after sorting multiple candidate individuals in the current layer according to their crowding distance from highest to lowest, the computer device prioritizes selecting the highest-ranked individual as the third population based on the remaining space. For example, if the space of the third population can accommodate 100 individuals, after the computer device filters individuals from layers F1 and F2 into the third population, the remaining space of the third population can still accommodate 10 individuals. Layer F3 has 12 individuals. The crowding distance of these 12 individuals is calculated, and the 12 individuals are sorted according to their crowding distance from highest to lowest. The computer device selects the top 10 individuals to enter the third population, thereby ensuring that the third population includes the candidate individuals with the highest crowding distance in the current layer within the space constraints, completing the dynamic expansion and optimization of the population.

[0139] In one exemplary embodiment, such as Figure 5 As shown, the above-mentioned coupling optimization method for quantum parameters further includes:

[0140] S501, compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0141] In this embodiment, after obtaining the third population, the computer device employs a binary tournament strategy, randomly selecting any two individuals from the third population to compare their non-dominance level and crowding distance. Individuals with both high non-dominance level and high crowding distance are selected as the fifth population. Specifically, the computer device prioritizes comparing non-dominance levels, with individuals of lower levels being superior (e.g., individuals in layer F1 have a rank of 1, while individuals in layer F2 have a rank of 2, so individuals in layer F1 are superior to individuals in layer F2). If the non-dominance levels are the same, the crowding distance is compared, with individuals of higher crowding distance being superior (e.g., individuals with a crowding distance of 2 are superior to individuals with a crowding distance of 1). This process is repeated until the selected individuals meet the preset number for the fifth population.

[0142] S502, perform crossover and mutation operations on individuals in the fifth population to generate the fourth population.

[0143] The crossover operation uses simulated binary crossover; the mutation operation uses polynomial mutation.

[0144] In this embodiment, after obtaining the fifth population, the computer device performs crossover and mutation operations on each individual in the fifth population. The crossover operation employs simulated binary crossover, which generates offspring by probabilistically exchanging the parameter components of two parent individuals. For example, two individuals (such as individual 1 and individual 2) are randomly selected from the fifth population as parents, where the parameters of individual 1 are (10, 5, 0.5) and the parameters of individual 2 are (15, 4, 0.6). A simulated binary crossover operation is performed with a probability of 0.9, randomly selecting some parameter components to exchange. For example, only the transition time is exchanged. The voltage step width W is used (keeping the number of steps N unchanged). Offspring are then generated based on a weighted average of the parent values ​​and a random perturbation, resulting in offspring of individual 1 with parameters (12, 5, 0.55) and offspring of individual 2 with parameters (13, 4, 0.55). Mutation is performed using polynomial mutation, applying a random perturbation to one or more parameters of an individual with a low probability. Polynomial mutation introduces new genetic material, helping the population escape potential local optima and ensuring global exploration capabilities during the search process. For example, if the offspring are mutated with a probability of 0.1, for the parameters (12, 5, 0.55) in offspring 1, assuming the random perturbation occurs during the transition time... , =13, then the parameters in offspring 1 become (13, 5, 0.55); assuming offspring 2 does not mutate, the parameters in offspring 2 are (13, 4, 0.55). After the mutation operation, the new offspring individuals explore new regions (such as the transition time). (Changed from 12 to 13).

[0145] In one exemplary embodiment, such as Figure 6 As shown, the above-mentioned coupling optimization method for quantum parameters further includes:

[0146] S601, a diversity assessment was conducted on the first population, and the assessment results were obtained.

[0147] In this embodiment, after obtaining the first population, the computer device uses diversity assessment indicators to quantitatively analyze the quality of the first population and obtains the assessment result. The diversity assessment indicators include not only the similarity calculation between individuals, but also Hamming distance statistics and the spatial distribution density of individuals in the solution space. For example, the computer device can calculate the similarity of individuals in the first population, using methods such as cosine similarity and Euclidean distance to traverse any two individuals to obtain a similarity matrix; subsequently, it uses statistical measures such as average similarity and similarity variance to calculate the degree of similarity between individuals. The computer device can also choose to assess the diversity of individual distribution within the solution space of the first population, i.e., by calculating indicators such as the standard deviation of individual coordinates and the number of clusters to determine the dispersion of the population in the solution space. Finally, these quantitative indicators are integrated into a comprehensive assessment score, i.e., the assessment result.

[0148] S602, if the evaluation results indicate that the first population does not meet the preset diversity requirements, then the initial population is re-initialized.

[0149] In this embodiment, the computer device judges the evaluation result against the preset diversity requirements. If the evaluation result indicates that the first population does not meet the preset diversity requirements, the initial population is re-initialized. This embodiment relates to a method for re-initializing the initial population, which is similar to the method described above. Figures 2-5 The calculation methods described in any implementation are basically the same. For details, please refer to the foregoing explanation, which will not be repeated here.

[0150] S603, if the evaluation result indicates that the first population meets the preset diversity requirements, then perform the step of spatial mapping of each individual in the first population according to the preset coupling index model to obtain the second population.

[0151] In this embodiment, the computer device judges the evaluation results against the preset diversity requirements. If the evaluation results indicate that the first group meets the preset diversity requirements, such as a low average similarity and a dispersed spatial distribution of individuals in the solution space, it indicates that the diversity of the first group is high. Then, the step of spatially mapping each individual in the first group according to a preset coupling index model is executed to obtain the second group. This embodiment relates to a method for spatially mapping each individual in the first group according to a preset coupling index model to obtain the second group, which is similar to the aforementioned method. Figures 2-5 The calculation methods described in any implementation are basically the same. For details, please refer to the foregoing explanation, which will not be repeated here.

[0152] In one exemplary embodiment, such as Figure 7 As shown, "initializing the initial population" in S201 above includes:

[0153] S701 sets the initial population size.

[0154] In this embodiment, the computer device determines the population size of the initial population by comprehensively considering factors such as the steady-state accuracy, dynamic response performance, and actual physical constraints of the quantum voltage synthesis system.

[0155] S702, based on preset physical constraints, mathematical characteristics and transition process time conditions, defines the feasible domain range of the first parameter of each individual in the initial population.

[0156] In this embodiment of the application, after determining the population size of the initial population, the computer device then defines the feasible domain range of the first parameter of each individual in the initial population based on preset physical constraints (such as hardware conditions such as the response speed of the Joseph array), mathematical characteristics (i.e., the coupling relationship between voltage step width, number of steps and transition time) and transition time conditions (i.e., the ratio of transition time to voltage step width is less than 10%).

[0157] S703 employs a uniform random sampling strategy or a Latin hypercube sampling algorithm to assign values ​​to the first parameter of each individual in the initial population, ensuring that the first parameter of each individual falls within the feasible region.

[0158] In this embodiment, after obtaining the feasible region of each parameter in the first parameter, the computer device can use a uniform random sampling strategy or a Latin hypercube sampling algorithm to assign values ​​to the first parameters (voltage step width, number of steps, and transition time) of each individual in the initial population, ensuring that the first parameters of each individual are within the feasible region. For example, the computer device uses a uniform random sampling strategy to independently generate the parameter values ​​of each individual within the feasible region of each parameter in the first parameter, ensuring a uniform distribution of the initial population in the solution space and avoiding premature aggregation of initial solutions in local regions. To further improve the initialization quality, advanced experimental design methods such as Latin hypercube sampling can be used to systematically perform hierarchical sampling of the parameter space, ensuring that the projection of the population in each dimension uniformly covers the entire parameter range, thereby significantly enhancing the global exploration capability of the population.

[0159] In summary, based on all the above embodiments, a method for optimizing the coupling of quantum parameters is also provided, such as... Figure 8 As shown, the method includes:

[0160] S801, Set the initial population size;

[0161] S802, based on preset physical constraints, mathematical characteristics and transition process time conditions, defines the feasible domain range of the first parameter of each individual in the initial population;

[0162] S803 uses a uniform random sampling strategy or a Latin hypercube sampling algorithm to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region, thus obtaining the first population after initialization.

[0163] S804, a diversity assessment was conducted on the first population, and the assessment results were obtained;

[0164] S805, if the assessment results do not meet the diversity requirements, proceed to S806; if the assessment results meet the diversity requirements, proceed to S807.

[0165] S806, If the evaluation results indicate that the first population does not meet the preset diversity requirements, the initial population shall be re-initialized.

[0166] S807, If the evaluation result indicates that the first population meets the preset diversity requirements, then perform the step of spatial mapping of each individual in the first population according to the preset coupling index model to obtain the second population.

[0167] S808, based on the preset coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group;

[0168] S809, for each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined according to the comparison results.

[0169] S810, sort the individuals in the second population in descending order of non-dominance level;

[0170] S811, select the top-ranked individuals from the sorted second population as multiple candidate individuals;

[0171] S812, If the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance;

[0172] S813: Select individuals with the highest crowding distance from multiple candidate individuals as individuals in the third population.

[0173] S814, compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0174] S815, perform crossover and mutation operations on individuals in the fifth population to generate the fourth population;

[0175] S816 performs iterative optimization based on the fourth population and outputs the second parameter of the best individual during the iterative optimization process after the iterative optimization is completed.

[0176] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0177] Based on the same inventive concept, this application also provides an AC quantum parameter coupling optimization device for implementing the above-described AC quantum parameter coupling optimization method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations in one or more AC quantum parameter coupling optimization device embodiments provided below can be found in the limitations of the AC quantum parameter coupling optimization method described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 9 As shown, a coupling optimization device for alternating quantum parameters is provided, comprising:

[0179] Initialization module 11 is used to initialize the initial population to obtain the first population after initialization;

[0180] The mapping module 12 is used to perform spatial mapping on each individual in the first group according to the preset coupling index model to obtain the second group.

[0181] The screening module 13 is used to screen individuals in the second population based on their non-dominance level and crowding distance to obtain a third population.

[0182] Crossover module 14 is used to perform crossover and mutation operations on individuals in the third population to generate the fourth population;

[0183] Iteration module 15 is used to perform iterative optimization based on the fourth population, and outputs the second parameter of the best individual in the iterative optimization process after the iterative optimization is completed.

[0184] In one embodiment, the filtering module 13 includes:

[0185] The stratification unit is used to perform a stratified comparison of the maximum effective value of AC voltage and the minimum transition time of each pair of individuals in the second population according to the non-dominated sorting mechanism, and to determine the non-dominated level of each individual based on the comparison results.

[0186] The sorting unit is used to sort the individuals in the second population in descending order of non-dominance level, and extract a preset number of individuals from the sorted second population according to the crowding distance of each individual to obtain the third population.

[0187] In one embodiment, the sorting unit includes:

[0188] The first sorting subunit is used to select the top-ranked individuals from the sorted second population as multiple candidate individuals.

[0189] The second sorting subunit is used to sort multiple candidate individuals in descending order of crowding distance if the number of multiple candidate individuals is greater than a preset number.

[0190] The third sorting subunit is used to select individuals with the highest crowding distance from multiple candidate individuals as individuals in the third population.

[0191] In one embodiment, the above-mentioned coupling optimization device for the quantum parameters further includes:

[0192] The intermediate module 16 is used to compare the non-dominance level and crowding distance of every two individuals in the third group, and select individuals with high non-dominance level and high crowding distance as the fifth group.

[0193] Crossover module 14 is also used to perform crossover and mutation operations on individuals in the fifth population to generate the fourth population.

[0194] In one embodiment, the initialization module 11 further includes:

[0195] The first assessment unit is used to assess the diversity of the first population and obtain the assessment results.

[0196] The second evaluation unit is used to re-initialize the initial population if the evaluation result indicates that the first population does not meet the preset diversity requirements.

[0197] The third evaluation unit is used to perform the step of spatial mapping of each individual in the first population according to the preset coupling index model to obtain the second population if the evaluation result indicates that the first population meets the preset diversity requirements.

[0198] In one embodiment, the initialization module 11 further includes:

[0199] The first initialization unit is used to set the initial population size.

[0200] The second initialization unit is used to define the feasible domain range of the first parameter of each individual in the initial population according to the preset physical constraints, mathematical characteristics and transition process time conditions.

[0201] The third initialization unit is used to assign values ​​to the first parameters of each individual in the initial population using a uniform random sampling strategy or a Latin hypercube sampling algorithm, so that the first parameters of each individual are within the feasible region.

[0202] Each module in the aforementioned coupling optimization device for quantum parameters can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0203] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0204] The initial population is initialized to obtain the first population after initialization; the first population consists of N individuals, each representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time;

[0205] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group; each individual in the second group represents the second parameter of the AC quantum signal to be optimized; the second parameter includes voltage step width, number of steps, transition time, maximizing the effective value of AC voltage, and minimizing the transition time;

[0206] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0207] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0208] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0209] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0210] For each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined based on the comparison results.

[0211] Sort the individuals in the second population in descending order of non-dominance level, and extract a predetermined number of individuals from the sorted second population based on the crowding distance of each individual to obtain the third population.

[0212] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0213] Select the top-ranked individuals from the second group after sorting as multiple candidate individuals;

[0214] If the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance;

[0215] Individuals with the highest crowding distance from multiple candidates are selected as individuals in the third group.

[0216] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0217] Compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0218] Crossover and mutation operations are performed on individuals in the third population to generate a fourth population, including:

[0219] Crossover and mutation operations are performed on individuals in the fifth population to generate the fourth population.

[0220] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0221] A diversity assessment was conducted on the first population, and the assessment results were obtained.

[0222] If the evaluation results indicate that the first population does not meet the preset diversity requirements, the initial population will be re-initialized.

[0223] If the evaluation results indicate that the first population meets the preset diversity requirements, then the step of spatially mapping each individual in the first population according to the preset coupling index model is executed to obtain the second population.

[0224] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0225] Set the initial population size;

[0226] Based on the preset physical constraints, mathematical characteristics, and transition time conditions, the feasible domain range of the first parameter of each individual in the initial population is defined.

[0227] A uniform random sampling strategy or a Latin hypercube sampling algorithm is used to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region.

[0228] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0229] The initial population is initialized to obtain the first population after initialization; the first population consists of N individuals, each representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time;

[0230] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group; each individual in the second group represents the second parameter of the AC quantum signal to be optimized; the second parameter includes voltage step width, number of steps, transition time, maximizing the effective value of AC voltage, and minimizing the transition time;

[0231] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0232] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0233] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0234] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0235] For each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined based on the comparison results.

[0236] Sort the individuals in the second population in descending order of non-dominance level, and extract a predetermined number of individuals from the sorted second population based on the crowding distance of each individual to obtain the third population.

[0237] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0238] Select the top-ranked individuals from the second group after sorting as multiple candidate individuals;

[0239] If the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance;

[0240] Individuals with the highest crowding distance from multiple candidates are selected as individuals in the third group.

[0241] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0242] Compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0243] Crossover and mutation operations are performed on individuals in the third population to generate a fourth population, including:

[0244] Crossover and mutation operations are performed on individuals in the fifth population to generate the fourth population.

[0245] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0246] A diversity assessment was conducted on the first population, and the assessment results were obtained.

[0247] If the evaluation results indicate that the first population does not meet the preset diversity requirements, the initial population will be re-initialized.

[0248] If the evaluation results indicate that the first population meets the preset diversity requirements, then the step of spatially mapping each individual in the first population according to the preset coupling index model is executed to obtain the second population.

[0249] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0250] Set the initial population size;

[0251] Based on the preset physical constraints, mathematical characteristics, and transition time conditions, the feasible domain range of the first parameter of each individual in the initial population is defined.

[0252] A uniform random sampling strategy or a Latin hypercube sampling algorithm is used to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region.

[0253] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0254] The initial population is initialized to obtain the first population after initialization; the first population consists of N individuals, each representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time;

[0255] Based on the pre-defined coupling index model, spatial mapping is performed on each individual in the first group to obtain the second group; each individual in the second group represents the second parameter of the AC quantum signal to be optimized; the second parameter includes voltage step width, number of steps, transition time, maximizing the effective value of AC voltage, and minimizing the transition time;

[0256] Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population.

[0257] Crossover and mutation operations are performed on individuals in the third population to generate the fourth population;

[0258] Iterative optimization is performed based on the fourth population, and the second parameter of the best individual in the iterative optimization process is output after the iterative optimization is completed.

[0259] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0260] For each pair of individuals in the second population, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated ranking mechanism, and the non-dominated level of each individual is determined based on the comparison results.

[0261] Sort the individuals in the second population in descending order of non-dominance level, and extract a predetermined number of individuals from the sorted second population based on the crowding distance of each individual to obtain the third population.

[0262] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0263] Select the top-ranked individuals from the second group after sorting as multiple candidate individuals;

[0264] If the number of multiple candidate individuals is greater than the preset number, sort the multiple candidate individuals in descending order of crowding distance;

[0265] Individuals with the highest crowding distance from multiple candidates are selected as individuals in the third group.

[0266] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0267] Compare the non-dominance level and crowding distance of every two individuals in the third group, and select the individuals with high non-dominance level and high crowding distance as the fifth group.

[0268] Crossover and mutation operations are performed on individuals in the third population to generate a fourth population, including:

[0269] Crossover and mutation operations are performed on individuals in the fifth population to generate the fourth population.

[0270] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0271] A diversity assessment was conducted on the first population, and the assessment results were obtained.

[0272] If the evaluation results indicate that the first population does not meet the preset diversity requirements, the initial population will be re-initialized.

[0273] If the evaluation results indicate that the first population meets the preset diversity requirements, then the step of spatially mapping each individual in the first population according to the preset coupling index model is executed to obtain the second population.

[0274] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0275] Set the initial population size;

[0276] Based on the preset physical constraints, mathematical characteristics, and transition time conditions, the feasible domain range of the first parameter of each individual in the initial population is defined.

[0277] A uniform random sampling strategy or a Latin hypercube sampling algorithm is used to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region.

[0278] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0279] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0280] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the coupling of quantum parameters, characterized in that, The method includes: The initial population is initialized to obtain the first population after initialization; the first population includes N individuals, each individual representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time; Based on a preset coupling index model, spatial mapping is performed on each individual in the first population to obtain a second population; each individual in the second population represents a second parameter of the AC quantum signal to be optimized; the second parameter includes the voltage step width, the number of steps, the transition process time, maximizing the effective value of the AC voltage, and minimizing the transition process time; The coupling index model includes: ； in, Represents the time constant; Indicates the number of steps; Indicates a time period; Indicates the duration of the transition process; Indicates constant coefficients; Indicates the transition time; Based on the non-dominance level and crowding distance of each individual in the second population, the second population is screened to obtain the third population; Crossover and mutation operations are performed on the individuals in the third population to generate a fourth population; Iterative optimization is performed based on the fourth group, and the second parameter of the optimal individual in the iterative optimization process is output after the iterative optimization is completed.

2. The method according to claim 1, characterized in that, The process of selecting individuals from the second population based on their non-dominance level and crowding distance to obtain a third population includes: For each pair of individuals in the second group, the maximum effective value of AC voltage and the minimum transition time of each pair of individuals are compared hierarchically according to the non-dominated sorting mechanism, and the non-dominated level of each individual is determined according to the comparison results. The individuals in the second population are sorted in descending order of non-dominance level, and a predetermined number of individuals are extracted from the sorted second population based on the crowding distance of each individual to obtain the third population.

3. The method according to claim 2, characterized in that, The step of extracting a predetermined number of individuals from the sorted second population based on the crowding distance to obtain a third population includes: Select the top-ranked individuals from the second group after sorting as multiple candidate individuals; If the number of the multiple candidate individuals is greater than the preset number, the multiple candidate individuals are sorted in descending order of crowding distance; The individuals with the highest crowding distance among the multiple candidate individuals are selected as individuals in the third group.

4. The method according to claim 3, characterized in that, The method further includes: In the third group, the non-dominance level and the crowding distance are compared between every two individuals. Individuals with high non-dominance level and high crowding distance are selected as the fifth group. The step of performing crossover and mutation operations on individuals in the third population to generate a fourth population includes: Crossover and mutation operations are performed on the individuals in the fifth population to generate the fourth population.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: A diversity assessment was performed on the first population, and the assessment results were obtained. If the evaluation result indicates that the first population does not meet the preset diversity requirements, then the initial population is re-initialized; If the evaluation result indicates that the first population meets the preset diversity requirements, then the step of spatially mapping each individual in the first population according to the preset coupling index model is executed to obtain the second population.

6. The method according to any one of claims 1-4, characterized in that, The initialization of the initial population includes: Set the initial population size; Based on preset physical constraints, mathematical characteristics, and transition process time conditions, the feasible domain range of the first parameter of each individual in the initial population is defined. A uniform random sampling strategy or a Latin hypercube sampling algorithm is used to assign values ​​to the first parameter of each individual in the initial population, so that the first parameter of each individual is within the feasible region.

7. A coupling optimization device for alternating quantum parameters, characterized in that, The device includes: An initialization module is used to initialize the initial population to obtain the first population after initialization; the first population includes N individuals, each individual representing the first parameter of the AC quantum signal to be optimized; the first parameter includes the voltage step width, the number of steps, and the transition time; The mapping module is used to spatially map each individual in the first population according to a preset coupling index model to obtain a second population; each individual in the second population represents a second parameter of the AC quantum signal to be optimized; the second parameter includes the voltage step width, the number of steps, the transition time, maximizing the effective value of the AC voltage, and minimizing the transition time; wherein, the coupling index model includes: ； in, Represents the time constant; Indicates the number of steps; Indicates a time period; Indicates the duration of the transition process; Indicates constant coefficients; Indicates the transition time; The filtering module is used to filter individuals in the second population based on their non-dominance level and crowding distance to obtain a third population. The crossover module is used to perform crossover and mutation operations on individuals in the third population to generate a fourth population. The iterative module is used to perform iterative optimization based on the fourth population, and output the second parameter of the optimal individual in the iterative optimization process after the iterative optimization is completed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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