A method for adaptive calibration and performance compensation of a topological qubit array

Abstract

The application discloses a kind of adaptive calibration and performance compensation methods of topological quantum bit array.The method measures the key performance indicators of each quantum bit in the array by executing automated calibration process, calculates the optimal operating parameters according to the measurement results and applies, periodically monitors performance indicators changes to detect parameter drift, triggers real-time compensation when detecting that the drift exceeds the preset threshold, and ensures the performance consistency of each quantum bit by performing performance balancing optimization at the array level.The application realizes the automation, real-time calibration and compensation of large-scale quantum bit array, and can be widely applied in the field of system-level performance optimization of topological quantum computing chip.

Description

Technical Field

[0001] This invention relates to the field of quantum computing and system calibration technology, specifically to an adaptive calibration and performance compensation method for topological qubit arrays. This invention is particularly applicable to scenarios involving automated calibration of large-scale topological qubit arrays, compensation for qubit performance differences, real-time tracking of environmental drift, and performance optimization of quantum computing systems. Background Technology

[0002] In large-scale topological qubit arrays, the physical parameters of each qubit vary during manufacturing, and their performance changes with fluctuations in environmental conditions such as temperature, magnetic field, and voltage. To ensure the overall performance of quantum computing, each qubit in the array needs to be calibrated and its performance compensated. However, as the number of qubits increases, traditional manual calibration methods are no longer sufficient. Existing calibration and compensation technologies can be mainly classified into the following categories: (a) Manual bit-by-bit calibration The operating parameters of each qubit are measured and adjusted manually. This type of scheme has the following problems: (1) slow calibration speed, which cannot meet the needs of large-scale arrays; (2) manual operation introduces errors; (3) real-time tracking compensation cannot be achieved; (4) high cost. (ii) Compensation based on pre-stored calibration parameters The parameters of each qubit are measured and stored during the chip testing phase, and the parameters are read and compensated during operation. This type of scheme has the following problems: (1) it cannot compensate for real-time drift during operation; (2) the number of stored parameters increases linearly with the number of qubits; (3) parameter updates are difficult; and (4) it cannot adapt to environmental changes. (III) Adaptive calibration based on machine learning The operating parameters of qubits are automatically optimized using machine learning algorithms. This type of scheme has the following problems: (1) it requires a large amount of training data; (2) the model training time is long; (3) the algorithm complexity is high; and (4) the real-time performance is poor. (iv) Real-time compensation based on feedback control By measuring the performance indicators of qubits in real time, the operating parameters are adjusted based on feedback. This type of scheme has the following problems: (1) the convergence speed of the feedback control algorithm is slow; (2) it is difficult to synchronize multi-channel parallel control; (3) the delay of the control system is large; and (4) it lacks global optimization capability. In summary, the common fundamental flaw of existing calibration and compensation techniques lies in the lack of a method that can simultaneously achieve automated, real-time, and scalable adaptive calibration and performance compensation for topological qubit arrays. Summary of the Invention

[0003] (a) Purpose of the invention This invention provides an adaptive calibration and performance compensation method for topological qubit arrays, aiming to solve the technical problems of slow speed, poor real-time performance, and insufficient scalability in existing calibration and compensation methods. (II) Technical Principles This invention is based on the principles of automatic calibration and feedback control theory. In this invention, adaptive calibration and performance compensation are achieved through the following physical chain: Automated calibration process: Through a preset calibration procedure, the key performance indicators of each quantum bit in the array (decoherence time, gate fidelity, resonant frequency, etc.) are automatically measured, and the optimal operating parameters are automatically calculated. Parameter drift detection: Through periodic measurement and statistical analysis, the drift trend of qubit parameters is detected and parameter changes are predicted. Real-time compensation execution: Based on the detected parameter drift, the operating parameters of the quantum bit (gate voltage, bias current, pulse amplitude, etc.) are adjusted in real time through feedback control to maintain the optimal operating state. Global performance optimization: Performance balancing optimization is performed at the array level to ensure the performance consistency of each qubit and prevent individual poor-performing qubits from becoming bottlenecks. The fundamental difference from existing technologies: This invention is not a "manual bit-by-bit calibration" scheme, but rather achieves adaptive calibration through automated processes, drift detection, and real-time compensation. (III) Technical Solution An adaptive calibration method for a topological qubit array, characterized by comprising the following steps: S1: Performs an automated calibration process to measure the key performance indicators of each quantum bit in the array; S2: Calculate the optimal operating parameters for each qubit based on the measurement results; S3: Apply the optimal operating parameters to each qubit; S4: Periodically monitor changes in the performance indicators of qubits and detect parameter drift; S5: Real-time compensation is triggered when parameter drift is detected to exceed a preset threshold; S6: Perform performance balancing optimization at the array level to ensure the performance consistency of each quantum bit. A performance compensation system for a topological qubit array, characterized in that it comprises: Calibrate the measurement unit to measure key performance indicators of qubits; The parameter calculation unit is used to calculate the optimal operating parameters based on the measurement results. The parameter storage unit is used to store the optimal operating parameters for each quantum bit. The drift detection unit is used to periodically monitor parameter changes and detect drift. The compensation execution unit is used to adjust the working parameters in real time based on the drift detection results; The global optimization unit is used to perform performance balancing optimization at the array level. (iv) Beneficial effects First, the automated calibration process significantly reduces human intervention, shortening calibration time by more than 90%. Second, drift detection and real-time compensation ensure that the qubits always operate in the optimal state. Third, parameter storage and rapid recovery support the system's rapid startup and fault recovery. Fourth, global performance optimization improves the overall performance consistency of the array. Fifth, the calibration and compensation process can be performed online without affecting the normal operation of quantum computing. Detailed Implementation

[0004] Example 1 (Automatic Calibration of a 64-Qubit Array) Step 1: Perform the automated calibration process. The decoherence times T1 and T2 and the gate fidelity F of the 64 qubits are automatically measured using a preset calibration procedure. Step 2: Calculate the optimal operating parameters. Based on the measurement results, calculate the optimal gate voltage V_g and bias current I_bias for each qubit. Step 3: Apply optimal parameters. Write the calculated optimal parameters into each qubit through the interconnect matrix. Step 4: Periodically monitor drift. Measure the resonant frequency f_q of each quantum bit every 10 minutes to detect frequency drift. Step 5: Trigger real-time compensation. When the resonant frequency of a certain quantum bit drifts by more than 1MHz, the gate voltage is automatically adjusted for compensation. Step 6: Global performance optimization. Analyze the performance distribution of all qubits and perform additional optimization on the qubits with lower performance. Example 2 (Adaptive Calibration of 256-qubit Array) Extending to a 256-qubit array, a time-division calibration strategy is adopted, grouping the qubits and performing calibration in turn to avoid the calibration process affecting quantum computing. Example 3 (Parameter Optimization Based on Machine Learning) Machine learning models are used to analyze historical calibration data and predict the changing trends of optimal parameters to achieve predictive calibration. Example 4 (Remote Cloud Calibration) The calibration data is uploaded to the cloud, and the parameters are optimized using cloud computing resources. The optimization results are then downloaded to the local system for execution. Attached Figure Description

[0005] Figure 1This is a flowchart of the adaptive calibration and performance compensation method for the topological quantum bit array of the present invention. Figure 2 This is a block diagram of the overall architecture of the performance compensation system of the present invention. Figure 3 This is a schematic diagram illustrating the principle of parameter drift detection and real-time compensation in this invention. Figure 4 This is a schematic diagram of the global performance optimization process of the present invention. Figure 5 This is a schematic diagram of a typical application scenario of the present invention. Terminology Explanation

[0006] Adaptive calibration: The process of automatically adjusting the operating parameters of a quantum bit based on real-time measurement results. Parameter drift: The shift in the operating parameters of a quantum bit as time or environmental conditions change. Real-time compensation: Adjusts operating parameters immediately upon detecting parameter drift to maintain optimal performance. Global performance optimization: The optimization process of balancing the performance of each qubit at the array level.

Claims

1. Claim 1: An adaptive calibration method for a topological qubit array, characterized in that, Includes the following steps: S1: Performs an automated calibration process to measure the key performance indicators of each quantum bit in the array; S2: Calculate the optimal operating parameters for each qubit based on the measurement results; S3: Apply the optimal operating parameters to each qubit; S4: Periodically monitor changes in the performance indicators of qubits and detect parameter drift; S5: Real-time compensation is triggered when parameter drift is detected to exceed a preset threshold; S6: Perform performance balancing optimization at the array level to ensure the performance consistency of each quantum bit.

2. Claim 2: A performance compensation system for a topological qubit array, characterized in that, include: Calibrate the measurement unit to measure key performance indicators of qubits; The parameter calculation unit is used to calculate the optimal operating parameters based on the measurement results. The parameter storage unit is used to store the optimal operating parameters for each quantum bit. The drift detection unit is used to periodically monitor parameter changes and detect drift. The compensation execution unit is used to adjust the working parameters in real time based on the drift detection results; The global optimization unit is used to perform performance balancing optimization at the array level.

3. Claim 3: The method according to claim 1, characterized in that, The key performance indicators include at least one of the following: decoherence time T1, T2, gate fidelity F, and resonant frequency f_q.

4. Claim 4: The method according to claim 1, characterized in that, The optimal operating parameters include at least one of gate voltage, bias current, and pulse amplitude.

5. Claim 5: The method according to claim 1, characterized in that, The periodic monitoring period is from 1 minute to 1 hour.

6. Claim 6: The method according to claim 1, characterized in that, The preset threshold is 5%-20% of the change in performance indicators.

7. Claim 7: The system according to claim 2 is characterized in that, The compensation execution unit uses a PID control algorithm to achieve real-time compensation.

8. Claim 8: The method according to claim 1, characterized in that, The global performance optimization adopts a min-max optimization strategy to maximize the performance of the worst-case qubit.

9. Claim 9: The method according to claim 1, characterized in that, The automated calibration process automatically stores the calibration results after calibration is completed, supporting rapid recovery.

10. Claim 10: The system according to claim 2 is characterized in that, The system supports calibration during quantum computing operations without affecting normal computation.

Images