A flux regulated crosstalk calibration method for a quantum bit

Abstract

The application discloses a flux regulation crosstalk calibration method for a quantum bit, and relates to the technical field of quantum technology. The method comprises the following steps: S1, obtaining initial calibration parameters of a target quantum system by using an initial crosstalk matrix; S2, generating a plurality of groups of random flux configuration signals containing flux regulation values by using the initial crosstalk matrix; S3, constructing a modified crosstalk matrix by using the random flux configuration signals; S4, obtaining modified calibration parameters by using the modified crosstalk matrix and performing error evaluation; if the error evaluation is met, the modification is completed; if the error evaluation is not met, the step S2 is returned. Compared with the prior art, the application has the advantages of improving calibration accuracy and reducing the complexity of the phase measurement experiment process by constructing a frequency offset matrix and modifying the crosstalk matrix, generating the modified crosstalk matrix by using the quasi-Monte Carlo method and the frequency offset matrix, and avoiding the high sampling complexity proportional to the square of the number of bits in the two-dimensional spectrum scanning.

Description

Technical Field

[0001] This invention relates to the field of quantum technology, and more specifically to a method for magnetic flux control crosstalk calibration for qubits. Background Technology

[0002] In the field of superconducting quantum computing, the construction of superconducting quantum computers relies on complex experimental equipment and intricate measurement and control parameters. Furthermore, ensuring the efficient and stable operation of the quantum computer requires a complex process to calibrate these parameters step by step. Current technologies primarily improve the efficiency of automated calibration by increasing the speed of two-dimensional spectral scanning. Traditional magnetic flux crosstalk calibration measurements employ a two-dimensional spectral scanning sampling method. Since its complexity is proportional to the square of the number of qubits, this method is exceptionally cumbersome and becomes one of the most time-consuming experimental steps. The large number of samples required for two-dimensional spectral scanning also contributes significantly to the time commitment.

[0003] Existing experimental schemes for flux crosstalk calibration involve simultaneously adjusting the flux bias of the crosstalk bit and the Z-bias of the compensation bit, calculating the crosstalk magnitude of the applied bit to the compensation bit by plotting a two-dimensional image and observing the excitation state of the bit. However, this method has several problems, such as the time-consuming nature of plotting the two-dimensional image and the difficulty in determining the adjustable range of each bit, leading to suboptimal data. Another method typically fixes the Z-pulse intensity on the compensation bit, changes the height of the compensation pulse on the target line, and uses quantum state tomography to measure the phase on the compensation bit. The compensation amplitude of the compensation bit corresponds to the measured phase, which is equal to the phase of the applied bit without an added pulse. The ratio of the compensation amplitude to the source pulse height is the crosstalk strength. The disadvantage of this method is the need for quantum state tomography, which requires numerous experimental steps, affecting experimental efficiency. Furthermore, the phase measurement is highly dependent on the bit dephase time, making it difficult to guarantee the accuracy of this method. Summary of the Invention

[0004] This invention provides a magnetic flux control crosstalk calibration method for qubits, which solves the problems of the complex process of obtaining crosstalk intensity and low data accuracy when using quantum state tomography to calibrate measurement and control parameters based on magnetic flux crosstalk methods in existing superconducting quantum computing processes.

[0005] This invention is achieved through the following technical solution: A method for flux modulation crosstalk calibration for qubits, the method comprising: Step S1: Preset an initial crosstalk matrix in the target quantum system, use the initial crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system, and label the calibration results as the initial calibration parameters; Step S2: Use the initial crosstalk matrix to generate several sets of random magnetic flux configuration signals containing magnetic flux control values, and use the quasi-Monte Carlo method to generate random magnetic flux variables for controlling the quantum bit channel in each set of random magnetic flux configuration signals; Step S3: Construct a frequency offset vector using a random flux variable in each group of random flux configuration signals, construct a frequency offset matrix using all frequency offset vectors, and construct a crosstalk correction matrix based on the frequency offset matrix; Step S4: Use the corrected crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system and label them as corrected calibration parameters. Perform error evaluation on the corrected calibration parameters. If the error evaluation is met, the correction is completed; otherwise, return to step S2.

[0006] Furthermore, the frequency offset matrix is ​​set to the following form: Let the frequency offset matrix be represented by Y, then the frequency offset matrix is: , Where M represents the number of rows in the matrix, N represents the number of columns in the matrix, m represents the ordinal number of the row, and i represents the ordinal number of the column; ∆f (m) i This represents the frequency offset vector in the m-th row and i-th column, where m is set to a value between 1 and M, and i is set to a value between 1 and N.

[0007] Furthermore, let the frequency offset vector of the m-th row be calculated in the following form: , Where W represents the initial crosstalk matrix, φ m ε represents the amplitude of the Z-channel control pulse of a qubit in the target quantum system. m Let ε represent a random variable that follows a normal distribution; m The value is set to be from 0 to σ. 2 The natural number between σ and W is set to represent the noise mean of the initial crosstalk matrix W.

[0008] Furthermore, the content of constructing the corrected crosstalk matrix based on the frequency offset matrix includes: Let the corrected crosstalk matrix be represented as W * Set the Z-excitation matrix and represent it as Φ; set the corrected crosstalk matrix W. * The matrix calculation formula is expressed as: , in The formula represents the square of the Frobenius norm.

[0009] Furthermore, the Z-channel control pulse amplitude φ is used to construct the Z-excitation matrix Φ; The Z-excitation matrix Φ is set to the following form: .

[0010] Furthermore, the vector form of the Z-channel control pulse amplitude is set as follows: , Where φ i The Z-channel control pulse amplitude of the i-th qubit is represented, and φ is set. i The value ranges from -1 to 1.

[0011] Furthermore, the error assessment includes: calculating the residual norm of the corrected crosstalk matrix, calculating the error assessment component based on the residual norm, and setting an assessment threshold for the error assessment component to determine whether the error assessment is met.

[0012] Furthermore, let the error evaluation component be represented by η, and let the calculation formula for the error evaluation component η be expressed as: The evaluation threshold is set to 10. -4 When η < 10 -4 At that time, the correction crosstalk matrix is ​​judged to meet the error assessment.

[0013] Furthermore, the generation process of the magnetic flux random variable includes: For each qubit channel in the target quantum system, a magnetic flux bias range is preset. A quasi-Monte Carlo sampling method is used to generate a low-difference sampling sequence covering the magnetic flux bias range in the multidimensional magnetic flux space of the qubit channel. In each qubit channel, the low-difference sampling sequence is used to generate several candidate magnetic flux values ​​in a sub-interval of the magnetic flux bias range. The candidate magnetic flux values ​​are mapped to a random magnetic flux variable to be applied to the corresponding qubit channel.

[0014] Furthermore, the process of generating the low-difference sampling sequence includes: The dimension of the multidimensional magnetic flux space is determined based on the number of qubits in the target quantum system. The magnetic flux bias range corresponding to each qubit channel is set as the boundary condition of the multidimensional magnetic flux space. A set of low-difference sampling points covering the entire magnetic flux bias range is generated sequentially according to the set number of sampling points.

[0015] Compared with existing technologies, this invention obtains crosstalk parameters directly based on quantization modeling methods by constructing a frequency offset matrix and correcting the crosstalk matrix, without relying on the cumbersome quantum state tomography process, thus reducing the dependence on bit phase measurement. At the same time, it uses a quasi-Monte Carlo method to generate random magnetic flux signals, and then generates a corrected crosstalk matrix through the frequency offset matrix, avoiding the high sampling complexity that is proportional to the square of the number of bits in two-dimensional spectral scanning. This has the advantages of improving calibration accuracy and reducing the complexity of the phase measurement experimental process. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0018] Example 1 like Figure 1 As shown, this embodiment is a method for flux manipulation crosstalk calibration of qubits, which includes: Step S1: Preset an initial crosstalk matrix in the target quantum system, use the initial crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system, and label the calibration results as the initial calibration parameters; Step S2: Use the initial crosstalk matrix to generate several sets of random magnetic flux configuration signals containing magnetic flux control values, and use the quasi-Monte Carlo method to generate random magnetic flux variables for controlling the quantum bit channel in each set of random magnetic flux configuration signals; Step S3: Construct a frequency offset vector using a random flux variable in each group of random flux configuration signals, construct a frequency offset matrix using all frequency offset vectors, and construct a crosstalk correction matrix based on the frequency offset matrix; Step S4: Use the corrected crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system and label them as corrected calibration parameters. Perform error evaluation on the corrected calibration parameters. If the error evaluation is met, the correction is completed; otherwise, return to step S2.

[0019] The initial crosstalk matrix represents a crosstalk matrix used for flux crosstalk calibration, obtained through historical experimental experience, existing physical modeling results, simulation calculations, or rapid coarse measurements. In this embodiment, this matrix is ​​not the final precise value, but rather serves as a starting point for subsequent corrections and optimizations, reducing the search range and experimental complexity of the calibration process. Using the initial crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system means performing conventional flux crosstalk calibration using the initial crosstalk matrix and obtaining the quantum parameter calibration results of the target quantum system. It should be noted that the corrected calibration parameters are similar to the initial calibration parameters; the corrected calibration parameters are obtained by performing conventional flux crosstalk calibration using the corrected crosstalk matrix and obtaining the quantum parameter calibration results of the target quantum system. In specific implementations, the quantum parameters may include quantum data such as qubit eigenfrequency, frequency offset, coherence time parameters, and control response parameters. The random flux configuration signal refers to a signal generated under a specific distribution containing one or more sets of flux control values ​​during the flux manipulation of qubits, instead of using a fixed or single flux bias value. This is achieved by introducing a certain degree of randomness or pseudo-randomness. These signals contain flux inputs with different amplitudes, phases, or bias levels, used to simulate and explore various crosstalk scenarios that may occur between qubits. The flux control value refers to the parameter that controls the frequency of the qubit, representing the magnitude of the flux set by the control system.

[0020] The quasi-Monte Carlo method is a statistical simulation method for complex systems using random numbers or random variables. In this embodiment, it is used to generate random variables of magnetic flux controlling the qubit channel. Its core is to use random numbers or random sampling techniques to generate samples conforming to a certain statistical distribution in the magnetic flux control parameter space for qubit crosstalk modeling. As a specific application, in practice, the quasi-Monte Carlo method can be used in a pseudo-random number generator to map the generated random numbers to the magnetic flux range, generating random magnetic flux values ​​for the target bit and the compensation bit. The quasi-Monte Carlo method can generate a large number of random samples, capturing the overall trend and variation law of crosstalk between bits through statistical analysis, rather than relying on single-point measurements. This can reduce deviations caused by bit dephase time limitations, noise interference, or experimental errors, making the final corrected crosstalk matrix closer to the actual system coupling state, improving the accuracy and reliability of calibration.

[0021] The frequency offset vector represents the frequency shift in the resonant frequency of each qubit caused by the magnetic flux bias. This frequency offset is obtained by mapping each set of random magnetic flux variables to the corresponding bit's frequency offset. The data is presented in vector form, where each dimension represents the frequency offset of one bit. Using the frequency offset matrix, matrix operations are only needed based on the bit's frequency offset under random magnetic flux perturbation, eliminating the need to directly measure the quantum state phase, thus significantly reducing experimental steps. Furthermore, a limited number of random magnetic flux samples can cover possible crosstalk between bits, and crosstalk correction can be achieved through matrix operations, significantly shortening experimental time.

[0022] Example 2 In this embodiment, the frequency offset matrix is ​​set to the following form: Let the frequency offset matrix be represented by Y, then the frequency offset matrix is: , Where M represents the number of rows in the matrix, N represents the number of columns in the matrix, m represents the ordinal number of the row, and i represents the ordinal number of the column; ∆f (m) i This represents the frequency offset vector in the m-th row and i-th column, where m is set to a value between 1 and M, and i is set to a value between 1 and N. Let the frequency offset vector of the m-th row be calculated in the following form: , Where W represents the initial crosstalk matrix, φ m ε represents the amplitude of the Z-channel control pulse of a qubit in the target quantum system. m Let ε represent a random variable that follows a normal distribution; m The value is set to be from 0 to σ. 2 The natural number between σ and W is set to represent the noise mean of the initial crosstalk matrix W.

[0023] The frequency offset matrix represents the frequency offset of N target qubits under M random magnetic flux configurations. It describes the overall impact of magnetic flux perturbation on the frequencies of different qubits and is subsequently used as input to calculate the correction crosstalk matrix, thereby suppressing mutual interference caused by frequency offset. Each row is a frequency offset vector, representing the overall offset mode under the same magnetic flux configuration; each column reflects the random frequency drift distribution of the corresponding qubit, used to statistically analyze its sensitivity. The matrix element ∆f m The frequency offset of a qubit is determined by both the random variable of magnetic flux and the frequency-flux nonlinear response curve of the qubit. The formula for calculating the frequency offset vector is: the m-th frequency offset vector is controlled by the amplitude φ of the pulse from the Z-channel. m After linear mapping of the sensitivity matrix W, a portion of noise ε is superimposed. m get.

[0024] Furthermore, as a feasible implementation method, the content of constructing the corrected crosstalk matrix based on the frequency offset matrix includes: Let the corrected crosstalk matrix be represented as W * Set the Z-excitation matrix and represent it as Φ; set the corrected crosstalk matrix W. * The matrix calculation formula is expressed as: , in The formula represents the square of the Frobenius norm.

[0025] The Z-excitation matrix represents the set of Z-channel excitation variables applied to the qubit in all M groups of experiments, with each row of Z-channels controlling the pulse amplitude φ. m This corresponds to the random magnetic flux variable in a single experiment. In the formula... The term represents the fitting error, requiring the corrected crosstalk matrix W to make the predicted result ΦW as close as possible to the observed result Y. The calculation aims to ensure that the calculated W accurately reflects the relationship between magnetic flux disturbance and frequency shift in the experiment. In the formula... The regularization term is represented by the squared Frobenius norm, which penalizes excessively large matrix elements. Its calculation aims to prevent overfitting, improve model stability and generalization ability, and suppress the influence of noise on W. It should be noted that for the calculation to hold, the number of rows in the frequency offset matrix Y must be the same as the number of rows in the Z-excitation matrix Φ, the number of columns in the frequency offset matrix Y must be the same as the number of columns in the initial crosstalk matrix W, and the number of columns in the Z-excitation matrix Φ must be the same as the number of rows in the initial crosstalk matrix W.

[0026] Furthermore, as a feasible implementation method, the Z-channel control pulse amplitude φ is used to construct the Z-excitation matrix Φ; The Z-excitation matrix Φ is set to the following form: ; The vector form of the Z-channel control pulse amplitude is set as follows: , Where φ i The Z-channel control pulse amplitude of the i-th qubit is represented, and φ is set. i The value ranges from -1 to 1.

[0027] The Z-excitation matrix Φ is a matrix composed of multiple sets of Z-channel control pulses, with each column φ... m This represents the set of Z-channel control pulse amplitudes applied to all qubits in the m-th experimental sampling. Each Z-channel control pulse amplitude corresponds to a frequency offset vector, which is calculated from the Z-channel control pulse amplitudes. The vector φ of the Z-channel control pulse amplitudes... mLet represent the Z-channel control pulse amplitude configuration for all qubits in the m-th experiment; the longitudinal vector form of the Z-channel control pulse amplitude represents the control input for N qubits. As the input vector, the frequency shift ∆f is predicted by multiplying it with the crosstalk matrix W. m The φ i This represents the amplitude of the Z-channel control pulse applied to the i-th qubit in a certain experiment. φ is set... i The values ​​range from -1 to 1, with negative values ​​indicating negative adjustment of the magnetic flux pulse relative to the reference current and positive values ​​indicating positive adjustment. Normalizing the value range helps stabilize the algorithm, prevents numerical overflow, and ensures comparability between different experimental groups. Constructing the Z-excitation matrix Φ allows for a systematic representation of the excitation quantities of all Z-channels, serving as the input for subsequent frequency offset prediction and crosstalk correction matrix solving. This matrix representation facilitates parallel computation of large-scale bit systems. In specific implementations, the Z-channel control pulse amplitude configuration may include pulse configuration parameters such as pulse amplitude, pulse offset point, pulse shape, and pulse duration.

[0028] Furthermore, as a feasible implementation method, the error assessment includes: calculating the residual norm of the corrected crosstalk matrix, calculating the error assessment component based on the residual norm, and setting an assessment threshold for the error assessment component to determine whether it meets the error assessment requirements.

[0029] In specific implementation, the residual norm can be defined as: R = Y - ΦW * , where ΦW * This represents the frequency offset matrix predicted by the corrected crosstalk matrix. The residual norm R represents the predicted frequency offset matrix ΦW. * The difference matrix between the observed frequency offset matrix Y and the error matrix Y. The use of the residual norm avoids potential biases caused by judging solely by absolute error, ensuring the comparability of calibration results for signals of different scales. The normalized error index can effectively suppress the influence of experimental noise or single outliers. In practical applications, as a feasible implementation, the total value of the residual norm in the corrected crosstalk matrix can be expressed as: .

[0030] Among them, residual norm The residual norm represents the energy magnitude of the overall difference between the prediction matrix and the actual observation matrix. A smaller residual norm indicates a higher energy level. * The closer the accuracy is to real observation data, the greater the accuracy. The dual summation process involves iterating through all experimental trials M and the number of qubits N, ensuring that the residual calculation covers all qubits under all experimental conditions, guaranteeing comprehensive evaluation metrics. The Y...mi This represents the actual observed frequency offset of the i-th qubit in the m-th experiment. (ΦW) * ) mi This represents the theoretically predicted frequency offset calculated based on the Z-channel excitation matrix and the corrected crosstalk matrix. The square root of the squared residual term is used to cancel out positive and negative errors, making points with larger deviations contribute more to the overall error and amplifying outliers.

[0031] Furthermore, as a feasible implementation, let the error evaluation component be represented by η, and let the calculation formula for the error evaluation component η be expressed as: The evaluation threshold is set to 10. -4 When η < 10 -4 At that time, the correction crosstalk matrix is ​​judged to meet the error assessment.

[0032] In the formula The average residual between a single qubit and the model prediction can be expressed as the residual norm of a single qubit. In the formula... This represents the reference energy for the frequency shift measured in the experiment. The error assessment component η is calculated using the normalized mean square residual form. When η approaches 0, it indicates a high degree of similarity between the predicted and observed values; when η approaches 1, it indicates that the predicted value calculation is unacceptable and can hardly explain the observed results; η < 10 -4 Immediately determine whether the corrected crosstalk matrix meets the error assessment. Using the 2-norm as the residual norm can balance large local deviations and overall trends, thus making noise control more stable.

[0033] Example 3 In this embodiment, the generation process of the magnetic flux random variable includes: For each qubit channel in the target quantum system, a magnetic flux bias range is preset. A quasi-Monte Carlo sampling method is used to generate a low-difference sampling sequence covering the magnetic flux bias range in the multidimensional magnetic flux space of the qubit channel. In each qubit channel, the low-difference sampling sequence is used to generate several candidate magnetic flux values ​​in a sub-interval of the magnetic flux bias range. The candidate magnetic flux values ​​are mapped to a random magnetic flux variable to be applied to the corresponding qubit channel.

[0034] On the Z channel of each qubit, an allowable flux control range is set according to empirical rules or industry averages to ensure that the sampled values ​​do not exceed the safe range of the physical device, avoiding over-control that could lead to qubit failure or frequency drift into an uncontrollable range. The candidate flux value represents the intermediate value of the generated flux random variable, a discrete value generated by a Halton sequence generator within a preset flux bias range and sub-intervals. Using a quasi-Monte Carlo method to generate low-discrepancy sequences achieves more uniform coverage in multidimensional space, reducing sample concentration or blank areas, improving sampling efficiency, and providing better coverage than traditional Monte Carlo methods with the same number of samples. The Halton sequence generator is a commonly used low-discrepancy sequence generator that ensures uniform and comprehensive distribution of sampling points across multiple qubit channels; when multiple qubits are controlled simultaneously, it avoids the problem of sparse random sampling distribution in high-dimensional space. The overall flux bias range is divided into multiple sub-intervals to ensure that different local areas are sampled and covered, avoiding concentration in certain intervals that could lead to missed local crosstalk features.

[0035] Furthermore, the process of generating the low-difference sampling sequence includes: The dimension of the multidimensional magnetic flux space is determined based on the number of qubits in the target quantum system. The magnetic flux bias range corresponding to each qubit channel is set as the boundary condition of the multidimensional magnetic flux space. A set of low-difference sampling points covering the entire magnetic flux bias range is generated sequentially according to the set number of sampling points.

[0036] The multidimensional flux space represents an independent flux bias degree of freedom corresponding to each qubit channel. Each dimension represents the Z-channel flux bias range of the corresponding bit, and each point in the entire multidimensional space represents a qubit joint flux configuration. The flux bias range of the qubit channel corresponds to the minimum and maximum values ​​of that dimension in the multidimensional space, ensuring that all generated sampling points are within the safe operating range allowed by the device, avoiding control current exceeding hardware limits, and covering the entire available flux range. In specific implementations, the required total number of sampling points M can be set. The sequence generator will sequentially generate M sampling points covering the entire multidimensional flux space. Each sampling point corresponds to a Z-channel joint pulse configuration, used to apply to the qubit system to generate frequency shift observations. Mapping the flux bias of each qubit to the boundary conditions of the multidimensional space achieves uniform coverage of the entire high-dimensional flux space.

[0037] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for crosstalk calibration using flux modulation for quantum bits, characterized in that, The method includes: Step S1: Preset an initial crosstalk matrix in the target quantum system, use the initial crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system, and label the calibration results as the initial calibration parameters; Step S2: Use the initial crosstalk matrix to generate several sets of random magnetic flux configuration signals containing magnetic flux control values, and use the quasi-Monte Carlo method to generate random magnetic flux variables for controlling the quantum bit channel in each set of random magnetic flux configuration signals; Step S3: Construct a frequency offset vector using a random flux variable in each group of random flux configuration signals, construct a frequency offset matrix using all frequency offset vectors, and construct a crosstalk correction matrix based on the frequency offset matrix; Step S4: Use the correction crosstalk matrix to obtain the calibration results of the quantum parameters of the target quantum system and label them as correction calibration parameters. Perform error evaluation on the correction calibration parameters. If the error evaluation is met, the correction is completed; otherwise, return to step S2. The frequency offset matrix is ​​set to the following form: Let the frequency offset matrix be represented by Y, then the frequency offset matrix is: , Where M represents the number of rows in the matrix, N represents the number of columns in the matrix, m represents the ordinal number of the row, and i represents the ordinal number of the column; ∆f (m) i This represents the frequency offset vector in the m-th row and i-th column, where m is set to a value between 1 and M, and i is set to a value between 1 and N. The content of constructing the corrected crosstalk matrix based on the frequency offset matrix includes: Let the corrected crosstalk matrix be represented as W * Set the Z-excitation matrix and represent it as Φ; set the corrected crosstalk matrix W. * The matrix calculation formula is expressed as: , in The formula represents the square of the Frobenius norm.

2. The method for flux modulation crosstalk calibration for qubits according to claim 1, characterized in that, Let the frequency offset vector of the m-th row be calculated in the following form: , Where W represents the initial crosstalk matrix, φ m ε represents the amplitude of the Z-channel control pulse of a qubit in the target quantum system. m Let ε represent a random variable that follows a normal distribution; m The value is set to be from 0 to σ. 2 The natural number between σ and W is set to represent the noise mean of the initial crosstalk matrix W.

3. The method for flux modulation crosstalk calibration for qubits according to claim 2, characterized in that, The Z-excitation matrix Φ is constructed using the Z-channel control pulse amplitude φ. The Z-excitation matrix Φ is set to the following form: .

4. The method for flux modulation crosstalk calibration for qubits according to claim 3, characterized in that, The vector form of the Z-channel control pulse amplitude is set as follows: , Where φ i This represents the amplitude of the Z-channel control pulse for the i-th qubit, and sets φ. i The value ranges from -1 to 1.

5. The method for flux modulation crosstalk calibration for qubits according to claim 1, characterized in that, The error assessment includes: calculating the residual norm of the corrected crosstalk matrix, calculating the error assessment component based on the residual norm, and setting an assessment threshold for the error assessment component to determine whether it meets the error assessment criteria.

6. A method for flux modulation crosstalk calibration for qubits according to claim 5, characterized in that, Let the error evaluation component be denoted as η, and let the formula for calculating the error evaluation component η be: The evaluation threshold is set to 10. -4 When η < 10 -4 At that time, the correction crosstalk matrix is ​​judged to meet the error assessment.

7. The method for flux modulation crosstalk calibration for qubits according to claim 1, characterized in that, The generation process of the magnetic flux random variable includes: For each qubit channel in the target quantum system, a magnetic flux bias range is preset. A quasi-Monte Carlo sampling method is used to generate a low-difference sampling sequence covering the magnetic flux bias range in the multidimensional magnetic flux space of the qubit channel. In each qubit channel, the low-difference sampling sequence is used to generate several candidate magnetic flux values ​​in a sub-interval of the magnetic flux bias range. The candidate magnetic flux values ​​are mapped to a random magnetic flux variable to be applied to the corresponding qubit channel.

8. A method for flux modulation crosstalk calibration for qubits according to claim 7, characterized in that, The process of generating the low-difference sampling sequence includes: The dimension of the multidimensional magnetic flux space is determined based on the number of qubits in the target quantum system. The magnetic flux bias range corresponding to each qubit channel is set as the boundary condition of the multidimensional magnetic flux space. A set of low-difference sampling points covering the entire magnetic flux bias range is generated sequentially according to the set number of sampling points.

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