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

Abstract

The present application provides an optimization method, electronic device, and storage medium for a quantum measurement system, relating to the field of superconducting quantum computing technology. The optimization method for a quantum measurement system includes: initializing and configuring the quantum measurement system; determining the type of operating point parameters and the operating point parameter space of a parametric amplifier in the quantum measurement system; obtaining a characteristic vector based on the type of operating point parameters and the operating point parameter space to construct a multi-objective optimization function; and optimizing the operating point parameter space based on the multi-objective optimization function. The optimization method establishes a systematic multi-objective optimization framework and utilizes advanced genetic optimization techniques to automate and simplify the selection of the operating point of the parametric amplifier, thereby achieving efficient reading of large-scale quantum bit chips.

Description

Technical Field

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

[0002] In quantum computing and quantum information processing, many approaches, such as quantum error correction and quantum simulation, require rapidly interleaved quantum logic gates and measurement operations. Ideally, qubit state reading and measurement should be non-destructive, fast, and high-fidelity. However, in superconducting quantum computing experiments, qubit systems are extremely susceptible to interference from the external environment. Therefore, a dedicated Purcell filter based on dispersive readout is required, coupled with a Josephson Parametric Amplifier (JPA) as a preamplifier readout link to achieve high-fidelity readout of multi-bit quantum states.

[0003] The traditional JPA operating point calibration method relies on repeated measurement of a two-dimensional graph, followed by a one-by-one screening of several operating points with high gain, low noise temperature, and appropriate bandwidth for verification. This method not only requires a lot of manual time, but the selected operating points can only characterize the basic performance of the parametric amplifier and are not necessarily optimal when actually applied to the situation where multiple bits are read together. Moreover, as the number of quantum bits on a single chip continues to increase, this method becomes inefficient and labor-intensive:

[0004] Currently, in superconducting quantum computing experiments, selecting the operating point of the parametric amplifier in the qubit readout link is a critical step. The mainstream calibration method involves manually scanning a wide range of pump frequencies to create a two-dimensional plot of the pump power and drive voltage relative to the readout frequency s21. The method then screens for operating points with high gain, low noise temperature, and suitable bandwidth for verification. Existing patents (CN109581099, "A Performance Test Method for a Josephson Parametric Amplifier"; CN116402151A, "Characterization Method, Apparatus, and Quantum Computing System for a Parametric Amplifier"; and CN117332863A, "Method for Calibrating and Optimizing a Parametric Amplifier") are all based on this basic calibration method. This method is feasible for smaller qubit sizes, but as qubit sizes scale, it requires a large number of repetitive experiments and becomes inefficient. Furthermore, in complex experimental environments, this method lacks flexibility, leading to redundant repetitive experiments and a waste of time.

[0005] Furthermore, traditional calibration methods are fundamentally mismatched with the practical requirements of multi-bit readout. Existing methods primarily optimize the microwave transmission characteristics of the readout cavity in a single dimension, serving only as a basic calibration for parametric amplifiers. This simplified evaluation system seriously overlooks the multi-dimensional nature of qubit readout. In actual qubit experiments, the readout performance of a single qubit requires a comprehensive evaluation of six key parameters: discrimination, signal-to-noise ratio, fidelity of the |0> and |1> states, and error rate. These parameters require systematic ensemble measurements. Even more challenging, in multi-bit frequency-multiplexed readout architectures, since multiple readout cavities share the same readout link, the readout performance of individual qubits can be complexly influenced by the operating characteristics of the JPA. Traditional calibration methods fail to fully account for this system-level coupling effect, resulting in the difficulty of selecting operating points that achieve optimal performance in actual multi-bit joint readout, severely hindering the overall performance improvement of quantum computing experimental systems.

[0006] Dependence on professionals. The current calibration process is overly dependent on professional technicians. The JPA working point optimization process requires operators to possess professional knowledge in multiple fields such as quantum physics, microwave engineering, and cryogenic testing. Cultivating a professional with sufficient knowledge reserves and experimental experience requires a lot of time and resources. This current situation of high reliance on professional experience not only limits the rapid development and promotion of quantum computing technology, but also poses a serious obstacle 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 heavily dependent on the personal experience of the operator. This subjectivity and uncertainty have become important factors restricting the industrialization of quantum computing technology. Summary of the Invention

[0007] In order to solve at least one of the above problems, the present application proposes an optimization method, electronic device and 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 type of operating point parameters and the parameter space of a parametric amplifier in the quantum measurement system; obtaining a characteristic vector based on the type of operating point parameters and the operating point parameter space to construct a multi-objective optimization function; and optimizing the operating point parameter space based on the multi-objective optimization function.

[0009] For example, in some embodiments of the present application, 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 reading 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 the π pulse in the single quantum bit gate in the quantum measurement system according to the operating frequency of each quantum bit; applying a π pulse to each quantum bit to determine the standard for distinguishing quantum states; calibrating the read parameters of the quantum measurement system according to the operating frequency of each quantum bit and the excitation amplitude of the π pulse; optimizing the control parameters of the single quantum bit gate through Rabi oscillation test and quantum state calibration; and adjusting each quantum bit 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 measurement system to be lower than a first set temperature.

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

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

[0013] For example, in some embodiments of the present application, determining the excitation amplitude of the π pulse in the single-qubit gate in the quantum measurement system based on 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 quantum bit to a corresponding operating point based on the optimized control parameters includes: determining the optimal operating frequency and bias point of each quantum bit; and shutting off the coupler in the quantum measurement system.

[0015] For example, in some embodiments of the present application, the operating point parameter space includes pump frequency, pump power and driving voltage, and determining the operating point parameter type and parameter space 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; and determining the driving 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 based on the operating point parameter space includes: preparing each quantum bit in the |0> state and the |1> state according to the standard for quantum state distinction and the operating point parameter space of the parametric amplifier in the quantum measurement system to obtain an IQ plane distribution; performing cluster analysis on the IQ plane distribution to extract the eigenvector, wherein the eigenvector includes: discrimination, signal-to-noise ratio, fidelity of the |0> state and the |1> state and the corresponding error rate; and constructing the multi-objective optimization function based on the eigenvector.

[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 to execute a corresponding algorithm on the operating point parameter space according to the number of the quantum bits; performing fast non-dominated sorting and evolutionary iterative update operations on the operating point parameter space to achieve a convergence state of the operating point parameter space, so that the IQ plane distribution results of the quantum bits meet the requirements of the multi-objective optimization function.

[0018] For example, in some embodiments of the present application, the corresponding algorithm to be executed on the operating point parameter space is determined based on the number of the quantum bits, including: when the number of the quantum bits is one, using a traditional genetic algorithm for the operating point parameter space; when the number of the quantum bits is two, using an NSGA-II algorithm for the operating point parameter space; when the number of the quantum bits is three or more, using an NSGA-III algorithm for the operating point parameter space.

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

[0020] According to the second aspect of the present application, at least one embodiment of the present application provides an electronic device, comprising: 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 a method as described in any one of the first aspects.

[0021] According to a third aspect of the present application, at least one embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in any one of the first aspects.

[0022] Through the above exemplary embodiments, the present application provides an optimization method for a quantum measurement system. By establishing a systematic multi-objective optimization framework and utilizing advanced genetic optimization techniques, the method automates and simplifies the selection of the operating point of a parametric amplifier, thereby achieving efficient reading of large-scale quantum bit chips. The method has at least one of the following effects:

[0023] 1. Significantly improve the efficiency of parametric amplifier operating point calibration: Based on the multi-objective optimization algorithm of NSGA-II / III, this algorithm eliminates the reliance on gradient information and efficiently explores the parameter space through sampling and evaluation of the objective function. The algorithm significantly improves the efficiency of operating point optimization through population evolution and dynamic construction of Pareto optimal sets, combined with a parallel computing architecture. This approach is particularly well-suited for implementation in complex quantum measurement environments, providing an effective means for rapid localization of the operating point.

[0024] Optimizing multi-qubit readout performance: By establishing a comprehensive evaluation system encompassing six key characteristics, including discrimination, signal-to-noise ratio, and state fidelity, we overcome the limitations of traditional methods that focus solely on a single performance metric. A multi-objective optimization function framework achieves an optimal balance between these key metrics, resulting in superior overall performance in actual multi-qubit joint readout. This multi-dimensional optimization approach provides a reliable guarantee for high-fidelity qubit readout.

[0025] 3. Improved utilization of quantum measurement resources: Automated calibration methods significantly reduce reliance on highly skilled operators, optimizing human resource allocation. Furthermore, improved calibration efficiency translates directly into increased effective experimental time, increasing the utilization of key experimental equipment, including the dilution refrigerator. This resource optimization effect is crucial for advancing quantum computing experiments.

[0026] 4. Scalable Calibration Scheme: This method achieves comprehensive optimization of multi-bit readout performance in a frequency-multiplexed readout architecture. This systematic optimization scheme not only meets current experimental requirements but also provides a technical foundation for the realization of larger-scale quantum systems. This scalability is of great value in advancing the development of quantum computing technology.

[0027] It should be understood that the foregoing general description and the following detailed description are merely illustrative and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By describing in detail exemplary embodiments thereof with reference to the accompanying drawings, 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 are not intended to limit the present application.

[0029] Figure 1 A flow chart showing an optimization method for a quantum measurement system according to an exemplary embodiment;

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

[0031] Figure 3 Schematic diagram of the evolutionary algebraic curve showing the optimized parameters;

[0032] Figure 4 A schematic diagram showing the evolution of the algebraic curve of the discrimination of 5 quantum bits;

[0033] Figure 5 This is a comparison chart of the 5-bit IQ distribution before and after optimization;

[0034] Figure 6 The following is a comparison of the 6 eigenvectors of 5 quantum bits before and after optimization;

[0035] Figure 7 The figure shows a structural diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof 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, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of these specific details, or other methods, components, materials, devices, etc. may be employed. 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 accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0039] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0040] Those skilled in the art will understand that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present application, and therefore cannot be used to limit the scope of protection of the present application.

[0041] Figure 1 A flow chart of an optimization method for a quantum measurement system according to an exemplary embodiment is shown.

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

[0043] In step S101 , the quantum measurement system is initialized and configured.

[0044] In a dilution refrigerator environment, the quantum measurement system needs to be initialized and configured. This initialization configuration includes steps S1011-S1017. During the initialization configuration, the temperature of the quantum measurement system needs to be controlled below a first set temperature, and necessary electromagnetic shielding measures need to be implemented. For example, the first set temperature is 20 mK.

[0045] This initialization process crucially impacts the reliability and stability of subsequent optimization, requiring careful attention to temperature fluctuations and electromagnetic interference. In multi-qubit readout systems, a stable and reliable initialization configuration is fundamental to achieving high-fidelity measurements. This step, through a systematic experimental process, ensures that subsequent optimization algorithms can be executed on a reliable experimental platform.

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

[0047] According to an exemplary embodiment, the filter's operating parameters include center frequency and bandwidth. Within the filter's set center frequency and bandwidth, a microwave source is used to scan microwave signals of varying frequencies and powers to obtain the read cavity's transmission coefficient, read power, and frequency. This process ensures the filter can effectively and selectively read the target frequency, providing a foundation for subsequent multi-qubit reads.

[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 quantum bit.

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

[0050] In step S1013, based on the operating frequency of each quantum bit, the quantum measurement system determines the excitation amplitude of the π pulse in the single quantum bit gate.

[0051] According to an example embodiment, the amplitude of the excitation pulse is swept according to the operating frequency of each qubit 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 quantum bit to determine a criterion for distinguishing quantum states.

[0053] According to an example embodiment, a π pulse is applied to prepare the target quantum bit in the |0> state and the |1> state, respectively, and the distribution on the IQ plane is obtained by a synchronous detection method, which is preliminarily determined as a standard for distinguishing quantum states.

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

[0055] According to an exemplary embodiment, reading parameters includes optimizing reading power, adjusting reading time windows, 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 testing and quantum state calibration.

[0057] According to an exemplary embodiment, optimizing the control parameters includes performing amplitude calibration and orthogonal amplitude calibration on the pulses of the pulse source to achieve high-fidelity quantum state operations. Through iterative optimization, the accuracy of the single-bit gate operation is ensured to meet the expected requirements.

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

[0059] According to an example embodiment, the optimal operating frequency and bias point of each quantum bit are determined, and based on the optimized control parameters, each quantum bit is adjusted to the corresponding operating point: that is, the optimal operating frequency and bias point; then the coupler in the quantum measurement system is turned off to minimize the crosstalk effect between bits.

[0060] In step S102, the operating point parameter type and operating point parameter space of the parametric amplifier in the quantum measurement system are determined.

[0061] According to an exemplary embodiment, the operating point parameter types include three key control parameters: pump frequency, pump power, and drive voltage. Specifically, the pump frequency range is determined based on the parametric amplifier's amplification mechanism, with the optimized pump frequency interval defined around twice the parametric amplifier's readout frequency. The pump power range is determined based on the parametric amplifier's device characteristics to avoid device saturation and nonlinear effects. The drive voltage range is determined based on the parametric amplifier's passband characteristics, taking into account the device's operating point stability.

[0062] This application leverages the full-space search capabilities of the genetic algorithm, eliminating the need for prior knowledge of the parametric amplifier's performance. The operating point parameter space is typically selected as the target full space. Furthermore, the operator can select any subset of these parameters for optimization based on specific experimental requirements and prior knowledge, effectively narrowing the search space.

[0063] In step S103 , according to the type of operating point parameters and the operating point parameter space, a feature vector is obtained to construct a multi-objective optimization function.

[0064] According to an exemplary embodiment, based on the operating point parameter type and operating point parameter space of the parametric amplifier determined in step S102, and according to the quantum state distinction criteria, each quantum bit is prepared in the |0> state and the |1> state to obtain an IQ plane distribution. The IQ plane distribution is clustered and analyzed using a kmeans clustering model to extract a six-dimensional eigenvector. The eigenvector includes: discrimination, signal-to-noise ratio, fidelity of the |0> state and the |1> state, and the corresponding error rate. A multi-objective optimization function is constructed based on the eigenvectors. These six-dimensional eigenvectors are synthesized using a carefully designed weighted loss function to form the final multi-objective optimization function, in which the weights of each parameter can be adjusted according to specific application requirements.

[0065] In step 104, the operating point parameter space is optimized according to the multi-objective optimization function.

[0066] According to an example embodiment, step S104 includes steps S1041 - S1044 .

[0067] Step S1041, parameter normalization calculation: unifying the parameter dimensions of the working point parameter space, including designing a suitable normalization function, processing the scale differences between parameters, and ensuring that all parameters are treated fairly during the optimization process.

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

[0069] According to example embodiments, when the number of qubits is one, a traditional genetic algorithm is used for the operating point parameter space. When the number of qubits is two, the NSGA-II algorithm is used for the operating point parameter space, which can effectively handle competition between targets. When the number of qubits is three or more, the NSGA-III algorithm is used for the operating point parameter space, and its reference point mechanism can better handle high-dimensional target spaces.

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

[0071] According to some embodiments, fast non-dominated sorting can achieve hierarchical classification of individuals in a population and establish a dominance metric for the solution: this involves constructing a dominance relationship matrix, calculating the dominance relationships between individuals, and ultimately forming a clear hierarchical structure.

[0072] Step S1044, evolutionary iterative update: performing an 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 selection operations to select high-quality individuals, crossover operations to combine excellent features, and mutation operations to maintain population diversity until the algorithm converges to a stable solution set, which can achieve a convergence state in the working point parameter space, such as Figure 4 As shown in Figure 3, the discrimination of 5 bits of the same feeder gradually converges after 2000 generations of evolution along the evolutionary algebraic curve. Figure 2 To optimize the two-dimensional spatial distribution of parameters, after genetic evolution iterations, the working point parameter space gradually converges. Figure 3 To optimize the parameters along the evolutionary algebraic curve, as the genetic evolution iterates, the working point parameter space gradually converges.

[0074] And according to the stable working point parameter space, each quantum bit is prepared in the |0> state and |1> state to obtain the IQ plane distribution results, which meet the requirements of the multi-objective optimization function, such as Figure 5 As shown, Figure 5 This is a comparison of the IQ plane distribution of 5 quantum bits 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 turned on after optimization is completed. Figure 6 This is a comparison chart of the 6 eigenvectors of 5 quantum bits before and after optimization. The left side is before optimization and the right side is after optimization. It can be seen that the eigenvector values ​​after optimization are better than the eigenvector values ​​before optimization.

[0075] In step S105 , a verification test is performed on the operating 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, stability testing is used to verify the tolerance of the operating point parameter space to parameter drift. Bandwidth testing is used to ensure that the operating point parameter space meets the requirements of multi-bit frequency multiplexing. Noise evaluation testing 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 operating point parameter space. This verification process ensures the reliability of the optimization results in practical applications.

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

[0079] According to an exemplary embodiment, the selected operating point parameter space is loaded into a quantum measurement system, and a real-time monitoring mechanism is established within the quantum measurement system. The method of this application is particularly suitable for quantum measurement systems with 5-10 qubits. Furthermore, the quantum measurement system also establishes a complete data recording and analysis process, providing a basis for subsequent system optimization and improvement.

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

[0081] See Figure 7 , Figure 7 An electronic device is provided, comprising 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 achieve the following Figure 1 The method and refinement scheme shown.

[0082] It should be understood that the above-described device embodiments are merely illustrative, and the devices disclosed herein may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0083] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0084] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor or chip may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, on-chip cache, off-chip memory, and storage may be any appropriate 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] If the 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 the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0086] The embodiment of the present application also provides a non-transitory computer storage medium storing a computer program, which, when executed by multiple processors, causes the processors to execute the following Figure 1 The method and refinement scheme 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 teaching of the content disclosed in this application, these principles can be applied to many other embodiments.

[0088] Furthermore, it should be noted that the aforementioned figures are merely illustrative of the processes included in the methods according to exemplary embodiments of the present application and are not intended to be limiting. It is readily understood that the processes illustrated in the aforementioned figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0089] The exemplary embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present application is intended to cover various modifications and equivalent configurations 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 operating point parameter types and operating point parameter space of the parametric amplifier in the quantum measurement system, wherein the operating point parameter types include pump frequency, pump power, and drive voltage, including: 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; determining a driving voltage range according to a passband characteristic of the parametric amplifier; According to the operating point parameter type and the operating point parameter space, a feature vector is obtained to construct a multi-objective optimization function, including: 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 the |0> state and the |1> state to obtain the IQ plane distribution; Performing cluster analysis on the IQ plane distribution to extract the feature vector, wherein the feature vector includes: 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 vector; Optimizing the operating point parameter space according to the multi-objective optimization function includes: Unifying the parameter dimensions of the working point parameter space; Determining, based on the number of 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.

2. The optimization method according to claim 1, wherein: The initialization configuration of the quantum measurement system includes: Configuring operating parameters of a filter in the quantum measurement system, and determining a transmission coefficient, a read power, and a frequency of a read cavity in the quantum measurement system; Adjusting the output frequency of a microwave source in the quantum measurement system to determine the operating frequency of each quantum bit; determining, according to the operating frequency of each qubit, an excitation amplitude of a π pulse in a single qubit gate of the quantum measurement system; Applying a π pulse 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, wherein: The initialization configuration of the quantum measurement system further includes: The temperature of the quantum measurement system is controlled to be lower than a first set temperature.

4. The optimization method according to claim 2, wherein: The operating parameters include a center frequency and a bandwidth. The configuring the operating parameters of the filter in the quantum measurement system and determining the transmission coefficient, read power, and frequency of the reading cavity in the quantum measurement system include: The microwave source is used to scan microwave signals within the center frequency and bandwidth set by the filter to obtain the transmission coefficient, reading power and frequency of the reading cavity.

5. The optimization method according to claim 2, wherein: The step of adjusting the output frequency of a microwave source in the quantum measurement system to determine the operating frequency of each quantum bit includes: 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 single quantum bit currently transitions from the |0> state to the |1> state, the reading cavity no longer resonates with the reading pulse, and the transmission coefficient reaches 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 to be the operating frequency of the current single quantum bit.

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

7. The optimization method according to claim 2, wherein: 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; A coupler in the quantum measurement system is turned off.

8. The optimization method according to claim 1, wherein: Determining, based on the number of 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 operating point parameter space; When the number of quantum bits is two, the NSGA-II algorithm is used for the operating point parameter space; When the number of quantum bits is three or more, the NSGA-III algorithm is used for the operating point parameter space.

9. The optimization method according to claim 1, wherein: Also includes: Verify and test the operating point parameter space of the parametric amplifier in the quantum measurement system, including: stability verification, bandwidth test and noise evaluation test.

10. 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 perform the method according to any one of claims 1 to 9.

11. 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 9 is implemented.

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