Optimization method for quantum measurement system, electronic equipment and storage medium

Abstract

The invention provides an optimization method for a quantum measurement system, electronic equipment and a storage medium, and relates to the technical field of superconducting quantum computing. The optimization method for the quantum measurement system comprises the following steps: initializing and configuring the quantum measurement system; determining a working point parameter type and a working point parameter space of a parameter amplifier in the quantum measurement system; according to the working point parameter type and the working point parameter space, obtaining a feature vector to construct a multi-objective optimization function; and optimizing the working point parameter space according to the multi-objective optimization function. According to the optimization method, a systematic multi-objective optimization framework is established, an advanced genetic optimization technology is utilized, selection of working points of a parameter amplifier is automated and simplified, and efficient reading of large-scale quantum bit chips is achieved.

Description

Technical Field

[0001] This application relates to the field of superconducting quantum computing technology. Specifically, it relates to an optimization method, an electronic device, and a storage medium for a quantum measurement system. Background Art

[0002] In quantum computing and quantum information processing, many schemes such as quantum error correction and quantum simulation require fast and interleaved quantum logic gates and measurement operations. The ideal quantum bit state reading and measurement should be non-destructive, fast, and high-fidelity. However, in superconducting quantum computing experiments, the quantum bit system is extremely vulnerable to external environmental interference. Therefore, it is necessary to design a dedicated Purcell filter based on dispersive reading, and then cooperate with a Josephson parametric amplifier (JPA) as a preamplifier in the reading link to achieve high-fidelity readout of multi-bit quantum states.

[0003] The traditional method for calibrating the working point of JPA relies on a two-dimensional graph of repeated measurements, and then several working points with large gain, small noise temperature, and appropriate bandwidth are selected one by one for verification. Such a method not only requires a large amount of manual time, but also the selected working points can only characterize the basic performance of the parametric amplifier, and may not be optimal when actually applied to the case of joint reading of multiple bits. Moreover, as the number of qubits on a single chip continues to increase, this method becomes inefficient and costly in terms of labor:

[0004] Currently, in superconducting quantum computing experiments, the selection of the working point of the parametric amplifier in the qubit reading link is a crucial step. The mainstream calibration method is to manually scan a two-dimensional graph of pump power and drive voltage versus the reading frequency s21 at a large range of pump frequencies in experiments, and then several working points with large gain, small noise temperature, and appropriate bandwidth are selected one by one for verification. Existing patents (CN109581099 A method for testing the performance of a Josephson parametric amplifier; CN116402151A A method for characterizing a parametric amplifier, a device, and a quantum computing system; CN117332863A A method for calibrating and optimizing a parametric amplifier) are all based on such a basic calibration method. Such a method is feasible when the qubit scale is small, but with the further expansion of the qubit scale, this calibration method requires a large number of repetitive experiments and is inefficient. In addition, in the face of a relatively complex experimental environment, this method lacks flexibility and is prone to redundant repetitive experiments and time waste.

[0005] Moreover, there is an essential mismatch between traditional calibration methods and the actual multi-bit reading requirements. Existing methods mainly optimize in a single dimension based on the microwave transmission characteristics of the read resonator, which can only be used as the basic calibration of the parametric amplifier. This simplified evaluation system seriously ignores the multi-dimensional characteristics of qubit reading. In actual qubit experiments, the reading performance of a single qubit needs to be comprehensively evaluated through six key parameters such as discrimination, signal-to-noise ratio, | 0 > state and | 1 > state fidelity and error rate, etc. These parameters need to be obtained through systematic ensemble measurements. More challenging is that in the multi-bit frequency multiplexing read architecture, since multiple read resonators share the same read link, the reading performance of each qubit will have complex mutual influences through the working characteristics of the JPA. Traditional calibration methods fail to fully consider this system-level coupling effect, resulting in the selected working point being difficult to achieve the optimal performance in actual multi-bit joint reading, severely restricting the overall performance improvement of the quantum computing experimental system.

[0006] Dependence on professionals. The current calibration process has an excessive dependence on professional technicians. The optimization process of the JPA working point requires the operator to have professional knowledge in multiple fields such as quantum physics, microwave engineering, and cryogenic testing. It takes a large amount of time and resources to cultivate a professional with sufficient knowledge reserves and experimental experience. This current situation of highly relying on professional experience not only restricts the rapid development and popularization of quantum computing technology but also brings serious obstacles to the expansion of the quantum computing technology ecosystem. The lack of standardized and automated working point optimization methods makes the quality of the working point calibration process highly dependent on the personal experience of the operator, and this subjectivity and uncertainty have become an important factor restricting the industrial development of quantum computing technology. Summary of the Invention

[0007] To solve at least one of the above problems, the present application proposes an optimization method, an electronic device, and a storage medium for a quantum measurement system.

[0008] According to the first aspect of the present application, at least one embodiment of the present application provides an optimization method for a quantum measurement system, including: initializing and configuring the quantum measurement system; determining the types and parameter spaces of the working point parameters of the parametric amplifier in the quantum measurement system; obtaining eigenvectors according to the types of the working point parameters and the working point parameter spaces to construct a multi-objective optimization function; and optimizing the working point parameter space according to the multi-objective optimization function.

[0009] For example, in some embodiments of the present application, the initializing and configuring the quantum measurement system includes: configuring the operating parameters of the filter in the quantum measurement system, and determining the transmission coefficient, read power, and frequency of the readout cavity in the quantum measurement system; adjusting the output frequency of the microwave source in the quantum measurement system to determine the operating frequency of each qubit; determining the excitation amplitude of the π pulse in the single-qubit gate in the quantum measurement system according to the operating frequency of each qubit; applying the π pulse to each qubit to determine the criterion for quantum state discrimination; calibrating the readout parameters of the quantum measurement system according to the operating frequency of each qubit and the excitation amplitude of the π pulse; optimizing the control parameters of the single-qubit gate through Rabi oscillation testing and quantum state calibration; and adjusting each qubit to the corresponding operating point based on the optimized control parameters.

[0010] For example, in some embodiments of the present application, the initializing and configuring the quantum measurement system further includes: controlling the temperature of the quantum test system to be lower than the first set temperature.

[0011] For example, in some embodiments of the present application, the operating parameters include the center frequency and bandwidth, and the configuring the operating parameters of the filter in the quantum measurement system and determining the transmission coefficient, read power, and frequency of the readout cavity in the quantum measurement system includes: scanning the microwave signal using the microwave source within the center frequency and bandwidth set by the filter to obtain the transmission coefficient, read power, and frequency of the readout cavity.

[0012] For example, in some embodiments of the present application, the adjusting the output frequency of the microwave source in the quantum measurement system to determine the operating frequency of each qubit includes: outputting an excitation pulse and a read pulse using the microwave source; adjusting the frequency of the excitation pulse; when the frequency of the excitation pulse is the same as the frequency of a single qubit, the current single qubit changes from the |0> state to the |1> state, the readout cavity is no longer resonant with the read pulse, and the transmission coefficient appears as a peak; and determining the bit frequency corresponding to the transmission coefficient at the current moment as the operating frequency of the current single qubit when the transmission coefficient appears as a peak.

[0013] For example, in some embodiments of the present application, the determining the excitation amplitude of the π pulse in the single-qubit gate in the quantum measurement system according to the operating frequency of each qubit includes: scanning the amplitude of the excitation pulse according to the operating frequency of each qubit to determine the excitation amplitude of the π pulse in the single-qubit gate.

[0014] For example, in some embodiments of the present application, adjusting each qubit to a corresponding operating point based on the optimized control parameters includes: determining the optimal operating frequency and bias point of each qubit; turning off the coupler in the quantum test system.

[0015] For example, in some embodiments of the present application, the operating point parameter space includes pump frequency, pump power, and drive voltage. Determining the types and parameter space of the operating point parameters of the parametric amplifier in the quantum measurement system includes: determining the pump frequency range according to the parametric amplification mechanism of the parametric amplifier; determining the pump power range according to the device characteristics of the parametric amplifier; determining the drive voltage range according to the passband characteristics of the parametric amplifier.

[0016] For example, in some embodiments of the present application, constructing a multi-objective optimization function according to the operating point parameter space includes: preparing each qubit in the |0> state and the |1> state according to the criteria of quantum state discrimination and the operating point parameter space of the parametric amplifier in the quantum measurement system to obtain the IQ plane distribution; performing clustering analysis on the IQ plane distribution to extract the feature vectors, where the feature vectors include: discrimination, signal-to-noise ratio, fidelity of the |0> state and the |1> state, and corresponding error rates; constructing the multi-objective optimization function according to the feature vectors.

[0017] For example, in some embodiments of the present application, optimizing the operating point parameter space according to the multi-objective optimization function includes: unifying the parameter dimensions of the operating point parameter space; determining the corresponding algorithm to be executed on the operating point parameter space according to the number of qubits; performing fast non-dominated sorting and evolutionary iterative update operations on the operating point parameter space to make the operating point parameter space reach a convergence state, so that the IQ plane distribution result of the qubit meets the requirements of the multi-objective optimization function.

[0018] For example, in some embodiments of the present application, determining the corresponding algorithm to be executed on the operating point parameter space according to the number of qubits includes: adopting a traditional genetic algorithm for the operating point parameter space when the number of qubits is one; adopting the NSGA-II algorithm for the operating point parameter space when the number of qubits is two; adopting the NSGA-III algorithm for the operating point parameter space when the number of qubits is three or more.

[0019] For example, in some embodiments of the present application, it further includes: verifying and testing the operating point parameter space of the parametric amplifier in the quantum measurement system, including: stability verification, bandwidth test, and noise evaluation test.

[0020] According to a second aspect of the present application, at least one embodiment of the present application provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of the first aspect.

[0021] According to a third aspect of the present application, at least one embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method according to any one of the first aspect is implemented.

[0022] Through the above exemplary embodiments, an optimization method for a quantum measurement system provided by the present application, by establishing a systematic multi-objective optimization framework and using advanced genetic optimization techniques, automates and simplifies the selection of the operating point of a parametric amplifier, and realizes the efficient reading of a large-scale quantum bit chip, having at least one of the following effects:

[0023] 1. Achieve a significant improvement in the calibration efficiency of the operating point of the parametric amplifier: Based on the multi-objective optimization algorithms of NSGA-II / III, it gets rid of the dependence on gradient information, and can efficiently explore the parameter space only through the sampling evaluation of the objective function. Through population evolution and the dynamic construction of the Pareto optimal set, combined with a parallel computing architecture, the algorithm greatly improves the efficiency of operating point optimization. This method is particularly suitable for implementation in complex quantum measurement environments and provides an effective way for the rapid positioning of the operating point.

[0024] 2. Optimize the reading performance of multiple qubits: By establishing a comprehensive evaluation system including six-dimensional features such as distinguishability, signal-to-noise ratio, and state fidelity, it breaks through the limitation of traditional methods that only focus on a single performance index. The multi-objective optimization function framework achieves the optimal balance between these key indicators, making it show better comprehensive performance in actual multi-bit joint reading. This optimization scheme based on multi-dimensional evaluation provides a reliable guarantee for the high-fidelity reading of quantum bits.

[0025] 3. Improve the utilization efficiency of quantum measurement resources: The automated calibration method significantly reduces the dependence on highly skilled operators and optimizes the allocation of human resources. At the same time, the improvement of calibration efficiency directly translates into an increase in effective experimental time and improves the utilization rate of key experimental equipment including dilution refrigerators. This resource optimization effect is of great significance for promoting the development of quantum computing experiments.

[0026] 4. Scalable calibration scheme: This method realizes the overall optimization of multi-bit reading performance in a frequency multiplexing reading architecture. The systematic optimization scheme not only meets the current experimental requirements but also provides a technical foundation for the realization of larger-scale quantum systems. This scalability is of great value for promoting the development of quantum computing technology.

[0027] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By referring to the accompanying drawings and describing in detail its exemplary embodiments, the above and other objects, features, and advantages of the present application will become more apparent. The drawings described below are only some embodiments of the present application and do not limit the present application.

[0029] Figure 1 Flowchart of an optimization method for a quantum measurement system showing an exemplary embodiment;

[0030] Figure 2 Schematic diagram showing the two-dimensional spatial distribution of optimization parameters;

[0031] Figure 3 Schematic diagram showing the curve of the number of generations of evolution of optimization parameters;

[0032] Figure 4 Schematic diagram showing the curve of the distinguishability of 5 qubits with the number of generations of evolution;

[0033] Figure 5 Comparison diagram of the IQ distributions of 5 bits before and after optimization;

[0034] Figure 6 Comparison diagram of 6 eigenvectors of 5 qubits before and after optimization;

[0035] Figure 7 Structural diagram of an electronic device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar parts, and thus their repeated description will be omitted.

[0037] The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of these specific details, or in other ways, components, materials, devices, etc. In these cases, well-known structures, methods, devices, implementations, materials, or operations will not be shown or described in detail.

[0038] The flowcharts shown in the drawings are only illustrative and not necessarily include all the contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0039] In the description and claims of this application and the above drawings, the terms "first", "second", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0040] Those skilled in the art can understand that the drawings are only schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, so they cannot be used to limit the protection scope of this application.

[0041] Figure 1 A flowchart of an optimization method for a quantum measurement system showing an exemplary embodiment.

[0042] As Figure 1 shown, the optimization method includes: steps S101 - S106.

[0043] In step S101, initialize and configure the quantum measurement system.

[0044] In the environment of a dilution refrigerator, it is necessary to initialize and configure the quantum measurement system, and the initialization and configuration include steps S1011 - S1017. When initializing and configuring, it is necessary to control the temperature of the quantum test system below the first set temperature and implement necessary electromagnetic shielding measures. For example, the first set temperature is 20 mK.

[0045] This initialization process has a decisive impact on the reliability and stability of subsequent optimization, and special attention needs to be paid to the control of temperature fluctuations and electromagnetic interference. In a multi-qubit readout system, a stable and reliable initialization configuration is the basis for achieving high-fidelity measurement. This step ensures that subsequent optimization algorithms can be executed on a reliable experimental platform through a systematic experimental process.

[0046] In step S1011, configure the operating parameters of the filter in the quantum measurement system, and determine the transmission coefficient, read power, and frequency of the readout cavity in the quantum measurement system.

[0047] According to an exemplary embodiment, the operating parameters of the filter include the center frequency and the bandwidth. Within the set center frequency and bandwidth of the filter, a microwave source is used to scan microwave signals of different frequencies and powers, and the transmission coefficient, read power, and frequency of the read cavity are obtained. This process ensures that the filter can effectively perform selective reading of the target frequency, providing a basis for subsequent reading of multiple qubits.

[0048] In step S1012, the output frequency of the microwave source in the quantum measurement system is adjusted to determine the operating frequency of each qubit.

[0049] According to an exemplary embodiment, an excitation pulse and a read pulse are output using a microwave source; the frequency of the excitation pulse is adjusted; when the frequency of the excitation pulse is the same as the frequency of a single qubit, the current single qubit transitions from | 0 > state to | 1 > state, and the read cavity in the quantum measurement system is no longer resonant with the read pulse. At this time, the transmission coefficient appears as a peak; when the transmission coefficient appears as a peak, the bit frequency of the transmission coefficient at the current moment is determined as the operating frequency of the qubit.

[0050] In step S1013, according to the operating frequency of each qubit, the excitation amplitude of the π pulse in the single qubit gate is determined in the quantum measurement system.

[0051] According to an exemplary embodiment, according to the operating frequency of each qubit, the amplitude of the excitation pulse is scanned to determine the excitation amplitude of the π pulse in the single qubit gate.

[0052] In step S1014, a π pulse is applied to each qubit to determine the criterion for quantum state discrimination.

[0053] According to an exemplary embodiment, a π pulse is applied to prepare the target qubit in | 0 > state and |1 > state respectively, and the distribution on the IQ plane is obtained through synchronous detection method to preliminarily determine the criterion for quantum state discrimination.

[0054] In step S1015, according to the operating frequency of each qubit and the excitation amplitude of the π pulse, the read parameters of the quantum measurement system are calibrated.

[0055] According to an exemplary embodiment, the read parameters include: optimizing the read power, adjusting the read time window, setting sampling parameters, etc., to establish an accurate measurement sequence. This step directly affects the accuracy and efficiency of subsequent measurements.

[0056] In step S1016, the control parameters of the single qubit gate are optimized through Rabi oscillation tests and quantum state calibration.

[0057] According to the exemplary embodiment, optimizing the control parameters includes performing amplitude calibration and quadrature amplitude calibration on the pulses of the pulse source to achieve high-fidelity quantum state operations. Through iterative optimization, ensure that the accuracy of single qubit gate operations meets the expected requirements.

[0058] In step S1017, based on the optimized control parameters, each qubit is adjusted to its corresponding operating point.

[0059] According to the exemplary embodiment, determine the optimal operating frequency and bias point of each qubit, and based on the optimized control parameters, adjust each qubit to its corresponding operating point: namely, the optimal operating frequency and bias point; then turn off the coupler in the quantum test system to minimize the crosstalk effect between qubits.

[0060] In step S102, determine the types of operating point parameters and the operating point parameter space of the parametric amplifier in the quantum measurement system.

[0061] According to the exemplary embodiment, the types of operating point parameters include three key control parameters: pump frequency, pump power, and drive voltage. Among them: determine the pump frequency range according to the parametric amplification mechanism of the parametric amplifier, and the optimization interval of the pump frequency is defined near twice the read frequency of the parametric amplifier. Determine the pump power range according to the device characteristics of the parametric amplifier to avoid device saturation and nonlinear effects. Determine the drive voltage range according to the passband characteristics of the parametric amplifier, and the stability of the device operating point needs to be considered.

[0062] This application utilizes the advantage of the full-space search of the genetic algorithm, does not require prior knowledge of the performance of the parametric amplifier, and the operating point parameter space is generally selected as the target full space. In addition, the operator can also select any subset of these parameters for optimization according to specific experimental requirements and prior knowledge, thereby effectively reducing the search space.

[0063] In step S103, according to the types of operating point parameters and the operating point parameter space, obtain eigenvectors to construct a multi-objective optimization function.

[0064] According to the exemplary embodiment, according to the types of operating point parameters and the operating point parameter space of the parametric amplifier determined in step S102, and according to the criteria for quantum state discrimination, each qubit is prepared in | 0 > state and | 1 > state to obtain the IQ plane distribution. Perform clustering analysis on the IQ plane distribution through the kmeans clustering model to extract six-dimensional eigenvectors. Among them, the eigenvectors include: discrimination, signal-to-noise ratio,| 0 > State sum | 1 > The fidelity of the state and the corresponding error rate. Construct a multi-objective optimization function based on the eigenvectors. These six-dimensional eigenvectors are integrated through a carefully designed weighted loss function to form the final multi-objective optimization function, where the weights of each parameter can be adjusted according to specific application requirements.

[0065] In step 104, optimize the working point parameter space according to the multi-objective optimization function.

[0066] According to the exemplary embodiment, step S104 includes steps S1041 - S1044.

[0067] Step S1041, parameter normalization calculation: Unify the dimension of the parameters in the working point parameter space, including designing a suitable normalization function, dealing with the scale differences between parameters, and ensuring that each parameter is treated fairly during the optimization process.

[0068] Step S1042, optimization algorithm selection and execution: Determine the corresponding algorithm to be executed on the working point parameter space according to the number of qubits, and adaptively select the most suitable algorithm.

[0069] According to the exemplary embodiment, when the number of qubits is one, the traditional genetic algorithm is adopted for the working point parameter space. When the number of qubits is two, the NSGA-II algorithm is adopted for the working point parameter space, which can effectively handle the competition relationship between objectives. When the number of qubits is three or more, the NSGA-III algorithm is adopted for the working point parameter space, and its reference point mechanism can better handle the high-dimensional objective space.

[0070] Step S1043, fast non-dominated sorting: Perform fast non-dominated sorting operation on the working point parameter space.

[0071] According to some embodiments, fast non-dominated sorting can achieve the hierarchical division of the population individuals and establish a dominance metric standard for solutions: It involves constructing a dominance relationship matrix, calculating the dominance relationship between individuals, and finally forming a clear hierarchical structure.

[0072] Step S1044, evolutionary iterative update: Perform evolutionary iterative update operation on the working point parameter space.

[0073] According to some embodiments, the evolutionary iterative update operation continuously improves the quality of the solution through carefully designed genetic operations, including selecting high-quality individuals in the selection operation, combining excellent features in the crossover operation, and maintaining the population diversity in the mutation operation. Until the algorithm converges to a stable solution set, it can achieve the convergence state of the working point parameter space, such as Figure 4As shown, the discrimination of 5 bits of the same feeder gradually converges with the curve of the number of evolutionary generations and converges gradually after 2000 generations of evolution. Figure 2 It is the two-dimensional spatial distribution diagram of the optimized parameters. After genetic evolution iteration, the working point parameter space gradually converges. Figure 3 It is the curve of the optimized parameters with the number of evolutionary generations. With genetic evolution iteration, the working point parameter space gradually converges.

[0074] And according to the stable working point parameter space, each qubit is prepared in | 0 > state and | 1> state. The obtained IQ plane distribution results meet the requirements of the multi-objective optimization function, as Figure 5 shown. Figure 5 It is the comparison diagram of the IQ plane distribution of 5 qubits before and after optimization. The upper row is before the parametric amplifier is turned off, and the lower row is after the parametric amplifier is optimized and turned on. Figure 6 It is the comparison diagram of 6 eigenvectors of 5 qubits before and after optimization. The left side is before optimization, and the right side is after optimization. It can be seen that the numerical values of the eigenvectors after optimization are better than those before optimization.

[0075] In step S105, verification tests are performed on the working point parameter space of the parametric amplifier in the quantum measurement system, including: stability verification, bandwidth test, and noise evaluation test.

[0076] According to some embodiments, the stability test is used to verify the tolerance of the working point parameter space to parameter drift. The bandwidth test is used to ensure that the working point parameter space meets the requirements of multi-bit frequency multiplexing. The noise evaluation test is used to measure the noise temperature of the quantum measurement system.

[0077] On this basis, combined with specific experimental requirements, the final configuration is selected from the verified working point parameter space. This verification process can ensure the reliability of the optimization results in practical applications.

[0078] In step S106, the selected working point parameter space is loaded into the quantum measurement system, and a real-time monitoring mechanism is established.

[0079] According to the exemplary embodiments, the selected working point parameter space is loaded into the quantum measurement system, and a real-time monitoring mechanism is established in the quantum measurement system. The method of the present application is particularly applicable to quantum measurement systems with 5 - 10 qubits. In addition, the quantum measurement system also establishes a complete data recording and analysis process to provide a basis for subsequent system optimization and improvement.

[0080] Figure 7 It shows the structural diagram of an electronic device provided by the present application.

[0081] Refer to Figure 7 .Figure 7 An electronic device is provided, including a processor and a memory. The memory stores computer instructions, and when the computer instructions are executed by the processor, the processor executes the computer instructions to implement the method and refinement scheme as follows: Figure 1 shown in the figure.

[0082] It should be understood that the above device embodiments are merely illustrative, and the devices disclosed in this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0083] In addition, without special instructions, in each embodiment of this application, each functional unit / module can be integrated in one unit / module, or each unit / module can exist physically alone, or two or more units / modules can be integrated together. The above integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0084] When the integrated unit / module is implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. Without special instructions, the processor or chip can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC, etc. Without special instructions, the on-chip cache, off-chip memory, and memory can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0085] When an integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments disclosed in this disclosure. And the aforementioned memory includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0086] Embodiments of this application also provide a non-transitory computer storage medium storing a computer program, which when executed by multiple processors, causes the processors to execute as Figure 1 the methods and refinement schemes shown.

[0087] It should be clearly understood that this application describes how to form and use specific examples, but this application is not limited to any details of these examples. On the contrary, based on the teachings of the content disclosed in this application, these principles can be applied to many other embodiments.

[0088] In addition, it should be noted that the above-mentioned drawings are only schematic illustrations of the processes included in the methods according to the exemplary embodiments of this application, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed, for example, synchronously or asynchronously in multiple modules.

[0089] The above specifically shows and describes the exemplary embodiments of this application. It should be understood that this application is not limited to the detailed structures, setting manners, or implementation methods described here; on the contrary, this application is intended to cover various modifications and equivalent settings included within the scope and purpose of the appended claims.

Claims

1. An optimization method for a quantum measurement system, characterized in that: include: Initializing and configuring the quantum measurement system; Determining the type and space of operating point parameters of the parametric amplifier in the quantum measurement system; According to the working point parameter type and the working point parameter space, a feature vector is obtained to construct a multi-objective optimization function; The operating point parameter space is optimized according to the multi-objective optimization function.

2. The optimization method according to claim 1, characterized in that: The initialization configuration of the quantum measurement system includes: Configuring the operating parameters of the filter in the quantum measurement system, and determining the transmission coefficient, read power and frequency of the read cavity in the quantum measurement system; Adjusting the output frequency of the microwave source in the quantum measurement system to determine the operating frequency of each quantum bit; Determining the excitation amplitude of a π pulse in a single quantum bit gate in the quantum measurement system according to the operating frequency of each quantum bit; Applying π pulses to each of the quantum bits to determine a criterion for distinguishing quantum states; Calibrate the read parameters of the quantum measurement system based on the operating frequency of each quantum bit and the excitation amplitude of the π pulse; Optimizing the control parameters of the single-qubit gate through Rabi oscillation testing and quantum state calibration; Based on the optimized control parameters, each quantum bit is adjusted to a corresponding operating point.

3. The optimization method according to claim 2, characterized in that: The initialization configuration of the quantum measurement system further includes: The temperature of the quantum testing system is controlled to be lower than a first set temperature.

4. The optimization method according to claim 2, characterized in that: The operating parameters include a center frequency and a bandwidth, the configuration includes operating parameters of a filter in the quantum measurement system, and determines a transmission coefficient, a read power and a frequency of a reading cavity in the quantum measurement system, including: Within the range of the center frequency and bandwidth set by the filter, the microwave source is used to scan the microwave signal to obtain the transmission coefficient, reading power and frequency of the reading cavity.

5. The optimization method according to claim 2, characterized in that: The adjustment includes the output frequency of the microwave source in the quantum measurement system to determine the operating frequency of each quantum bit, including: Utilizing the microwave source to output an excitation pulse and a readout pulse; adjusting the frequency of the excitation pulse; When the frequency of the excitation pulse is the same as the frequency of the single quantum bit, the current single quantum bit is | 0 > Transform to | 1 > state, the reading cavity no longer resonates with the reading pulse, and the transmission coefficient has a peak value; When a peak value appears in the transmission coefficient, the bit frequency corresponding to the transmission coefficient at the current moment is determined as the operating frequency of the current single quantum bit.

6. The optimization method according to claim 5, characterized in that: Determining the excitation amplitude of the π pulse in the single quantum bit gate of the quantum measurement system according to the operating frequency of each quantum bit includes: According to the operating frequency of each quantum bit, the amplitude of the excitation pulse is scanned to determine the excitation amplitude of the π pulse in the single quantum bit gate.

7. The optimization method according to claim 2, characterized in that: The step of adjusting each quantum bit to a corresponding operating point based on the optimized control parameters includes: Determining the optimal operating frequency and bias point for each of the qubits; The coupler in the quantum test system is turned off.

8. The optimization method according to claim 1, characterized in that: The types of operating point parameters include pump frequency, pump power and driving voltage, and the determining of the types of operating point parameters and the operating point parameter space of the parametric amplifier in the quantum measurement system includes: determining a pump frequency range according to a parametric amplification mechanism of the parametric amplifier; Determining a pump power range according to device characteristics of the parametric amplifier; The driving voltage range is determined according to the passband characteristics of the parameter amplifier.

9. The optimization method according to claim 2, characterized in that: The step of acquiring a feature vector to construct a multi-objective optimization function according to the type of the working point parameter and the working point parameter space includes: According to the quantum state distinction standard and the operating point parameter space of the parametric amplifier in the quantum measurement system, each quantum bit is prepared in | 0 > State and | 1 > state to obtain the IQ plane distribution; Perform cluster analysis on the IQ plane distribution to extract the feature vector, wherein the feature vector includes: discrimination, signal-to-noise ratio, | 0 > State and | 1 > The fidelity of the state and the corresponding error rate; The multi-objective optimization function is constructed according to the feature vector.

10. The optimization method according to claim 1, characterized in that: Optimizing the working point parameter space according to the multi-objective optimization function includes: Unifying the parameter dimensions of the working point parameter space; Determining, according to the number of the quantum bits, to execute a corresponding algorithm on the operating point parameter space; A fast non-dominated sorting and an evolutionary iterative update operation are performed on the operating point parameter space to achieve a convergence state of the operating point parameter space, so that the IQ plane distribution result of the quantum bit meets the requirements of the multi-objective optimization function.

11. The optimization method according to claim 10, characterized in that: Determining, according to the number of the quantum bits, to execute a corresponding algorithm on the operating point parameter space includes: When the number of the quantum bit is one, a traditional genetic algorithm is used for the working point parameter space; When the number of the quantum bits is two, the NSGA-II algorithm is used for the working point parameter space; When the number of quantum bits is three or more, the NSGA-III algorithm is used for the working point parameter space.

12. The optimization method according to claim 1, characterized in that: Also includes: The operating point parameter space of the parametric amplifier in the quantum measurement system is verified and tested, including: stability verification, bandwidth test and noise evaluation test.

13. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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