Control method and system for quantum gate parameter feedforward optimization

Abstract

The application discloses a control method and system for quantum gate parameter feedforward optimization, and relates to the technical fields of quantum information and quantum precision measurement. The control method comprises the following steps: determining a predicted error parameter of a preset time period according to an acquired error source parameter; determining an optimized quantum gate parameter of a quantum gate according to the predicted error parameter of the preset time period and a preset quantum gate parameter; generating a driving waveform parameter set corresponding to the optimized quantum gate parameter according to the optimized quantum gate parameter; and generating a target quantum gate corresponding to the driving waveform parameter set according to the driving waveform parameter set, so that the target quantum gate controls a target quantum bit. Through the control method, multi-source error compensation can be introduced before execution, end-to-end delay can be reduced, feedforward optimization of the quantum gate parameter can be realized, and therefore the fidelity and stability of the quantum gate can be significantly improved.

Description

Technical Field

[0001] This application relates to the fields of quantum information and quantum precision measurement technology, and more specifically, to a control method and system for quantum gate parameter feedforward optimization. Background Technology

[0002] Quantum computing physics approaches such as trapped ions and neutral atoms all rely on precise and stable driving fields (lasers or microwaves) to realize single-qubit and multi-qubit (e.g., Mølmer–Sørensen gates, CZ gates, etc.) quantum gates.

[0003] In practical systems, various errors exist between the driving source and the environment. For example, frequency and phase errors include: fast noise and servo peaks caused by the limited bandwidth of frequency-locked / phase-locked loops, slow drift of the reference cavity, phase jitter of the fiber and free space path, and microwave local oscillator phase noise, etc.

[0004] Amplitude and power errors include: laser intensity noise, AOM / EOM and power amplifier nonlinearity, and microwave link gain drift, etc.

[0005] External environmental errors include magnetic field drift, radio frequency trap voltage noise, and changes in the optical path (or cavity length) caused by temperature and mechanical vibration.

[0006] These errors are coupled to quantum gate operations through Rabi frequency, detuning, phase, and pulse shape, causing over-rotation (or under-rotation), phase stacking, out-of-mode excitation, and non-commutation errors, which limit the gate fidelity and scalability that can be realized.

[0007] Current engineering approaches mainly focus on improving source stability, offline or offline-online hybrid calibration, and robust waveforms and optimal control of quantum gates.

[0008] Improving source stability can be achieved through methods such as PDH frequency stabilization, noise absorbers, fiber phase noise cancellation, and microwave phase noise optimization.

[0009] Offline or offline-online hybrid calibration involves recalibrating amplitude and phase between successive experiments, as well as performing temperature drift compensation and other methods.

[0010] Quantum gate robust waveform and optimal control can include composite pulses, DRAG, GRAPE, CRAB, filter function shaping, etc., to reduce sensitivity to certain slow or specific spectral errors.

[0011] However, the applicant of this application has found that as the processing scale increases and the number of parallel gates increases, relying solely on methods such as "improving the source," "offline calibration," or "robust control" often results in insufficient bandwidth and excessive latency in the microsecond-level time period and MHz-level sensitive frequency band of quantum gate manipulation, making it difficult to combat transient disturbances during gate execution.

[0012] The content of the background section is merely technology known to the public and does not necessarily represent existing technology in the field. Summary of the Invention

[0013] This application aims to provide a control method and system for quantum gate parameter feedforward optimization to solve the above-mentioned technical problems.

[0014] According to one aspect of this application, a control method for feedforward optimization of quantum gate parameters is provided. The control method includes: determining prediction error parameters for a preset time period based on acquired error source parameters; determining optimized quantum gate parameters based on the prediction error parameters for the preset time period and preset quantum gate parameters; generating a set of driving waveform parameters for a target qubit corresponding to the optimized quantum gate parameters based on the optimized quantum gate parameters; and generating a target quantum gate corresponding to the driving waveform parameter set based on the driving waveform parameter set, so that the target quantum gate manipulates the target qubit.

[0015] According to another aspect of this application, a control system for feedforward optimization of quantum gate parameters is provided. The control system includes an error sensing module, an error fusion and prediction module, a gate parameter mapping module, and a waveform generation and execution module. The error sensing module acquires error source parameters; the error fusion and prediction module determines prediction error parameters for a preset time period based on the error source parameters; the gate parameter mapping module determines optimized quantum gate parameters based on the prediction error parameters for the preset time period and preset quantum gate parameters; the waveform generation and execution module generates a set of driving waveform parameters corresponding to the optimized quantum gate parameters for the target qubit, and further generates a target quantum gate corresponding to the driving waveform parameter set, so that the target quantum gate controls the target qubit.

[0016] According to another aspect of this application, this application also provides a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is capable of implementing the control method for quantum gate parameter feedforward optimization as described above.

[0017] According to another aspect of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the control method for quantum gate parameter feedforward optimization as described above.

[0018] According to another aspect of this application, this application also provides a computer program product, comprising: a computer program stored on a computer-readable storage medium; the computer program includes program instructions that, when executed by a computer, cause the computer to perform the control method for quantum gate parameter feedforward optimization as described above.

[0019] The control method of this application can introduce multi-source error compensation before the quantum gate is executed through the causal link of "error directly drives quantum gate parameters", reduce end-to-end delay, realize feedforward optimization of quantum gate parameters, and thus significantly improve the fidelity and stability of quantum gate. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a control method 1000 according to an embodiment of this application is shown;

[0022] Figure 2 A flowchart illustrating step S110 according to an embodiment of this application is shown;

[0023] Figure 3 A flowchart illustrating step S120 according to an embodiment of this application is shown;

[0024] Figure 4 A flowchart illustrating step S130 according to an embodiment of this application is shown;

[0025] Figure 5 A flowchart of a control method 2000 according to an embodiment of this application is shown;

[0026] Figure 6 A schematic diagram of a control system according to an embodiment of this application is shown;

[0027] Figure 7 Another schematic diagram of the control system according to an embodiment of this application is shown.

[0028] Explanation of reference numerals in the attached figures:

[0029] Control system 30;

[0030] Error sensing module 31; error fusion and prediction module 32; gate parameter mapping module 33; waveform generation and execution module 34; monitoring and acceptance module 35. Detailed Implementation

[0031] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0032] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, devices, etc. In these cases, well-known structures, methods, devices, implementations, materials, or operations will not be shown or described in detail.

[0033] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0034] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order.

[0035] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0036] The English terms used in this application, their full English names, and their corresponding Chinese definitions are as follows:

[0037] FPGA stands for Field-Programmable Gate Array.

[0038] SoC, System on Chip.

[0039] DSP stands for Digital Signal Processor.

[0040] PDH, Pound-Drever-Hall, Pound-Drever-Hall frequency stabilization technology.

[0041] LO, Local Oscillator.

[0042] AOM, Acousto-Optic Modulator.

[0043] EOM, Electro-Optic Modulator.

[0044] MPC stands for Model Predictive Control.

[0045] ARMA, Autoregressive Moving Average.

[0046] CRAB, Chopped Random Basis.

[0047] DRAG, Derivative Removal by AdiabaticGate, is a method for eliminating absolute hot derivatives.

[0048] GRAPE stands for Gradient Ascent Pulse Engineering.

[0049] DDS stands for Direct Digital Synthesis.

[0050] AWG stands for Arbitrary Waveform Generator.

[0051] IQ, In-phase and Quadrature, positive reciprocal modulation.

[0052] Existing technologies for improving source stability include phase noise feedforward based on PDH error signals, fiber-optic "phase noise denoisers," and intensity noise "noise absorbers." These solutions apply the error signal to the laser / microwave link itself to purify the output, but do not use the error signal to rewrite gate parameters in real time.

[0053] Offline or hybrid offline-online calibration, such as drift compensation and phase reference stabilization between successive experiments, includes measuring the reference signal before each cycle to reset the AOM / DDS phase, optical path phase locking, microwave phase reference alignment, etc. These schemes are granular at the "per experiment / sequence" level rather than "intra-gate / adjacent gate" level, making it difficult to cover microsecond-scale perturbations.

[0054] Quantum gate robust waveforms and optimal control, such as composite pulses for Rabi frequency / detuning errors, DRAG / optimal control waveforms, and peak avoidance design of filter functions, are often offline designs or low-speed adaptive methods. The preset waveforms cannot respond to rapid external disturbances in real time, and they do not receive external error signals as input during gate execution, making them difficult to handle fast noise and cross-channel interference.

[0055] See Figure 6 The control system 30 provided in this application for quantum gate parameter feedforward optimization may include an error sensing module 31, an error fusion and prediction module 32, a gate parameter mapping module 33, and a waveform generation and execution module 34. The control system 30 may be integrated into an FPGA+SoC architecture / or an FPGA+DSP architecture, or the control system 30 may be in the form of a distributed controller (master controller + local edge box).

[0056] The following is combined with Figure 6 This application describes a control method 1000 for feedforward optimization of quantum gate parameters.

[0057] See Figure 1 The control method 1000 may include steps S110-S140.

[0058] In step S110, the control system determines the prediction error parameters for a preset time period based on the acquired error source parameters.

[0059] According to the example embodiment, the error source parameters can be error parameter information of the source channel. The source channel can be one or a combination of phase and frequency channels, amplitude and power channels, and environmental channels. The error sensing module 31 can configure sampling error source parameters such as preset sampling frequency, source channel, and target bandwidth. For example, the preset sampling frequency range can be set to 100MS / s–1GS / s.

[0060] Optionally, the error source parameters can be one or a combination of phase and frequency channel error source parameters, amplitude and power channel error source parameters, and environmental channel error source parameters.

[0061] The phase and frequency channel error source parameters can be the error parameter information of the sampled phase and frequency channels. For example, the phase and frequency channel error source parameters may include laser PDH error signals (in-loop and out-of-loop), delayed self-heterodyne signals and heterodyne phase signals, as well as phase comparator / phase detector signals for microwave LO and reference comparison, etc. The error sensing module 31 can acquire the phase and frequency channel error source parameters by acquiring the error signal output by the lock-in amplifier.

[0062] The amplitude and power channel error source parameters can be the error parameter information of the sampled amplitude and power channels. For example, the amplitude and power channel error source parameters may include the optical power monitoring parameters of the AOM, the optical power monitoring parameters of the EOM, and the power monitoring parameters of the microwave detector. The error sensing module 31 can acquire the amplitude and power channel error source parameters by acquiring the error signal output by the photodetector.

[0063] The environmental channel error source parameters can be error parameter information of the sampled environmental channel. For example, environmental channel error source parameters may include magnetic field (Hall / coil monitoring) parameters, temperature (thermal / optical cavity temperature) parameters, vibration and accelerometer parameters, and power supply ripple monitoring parameters, etc. The error sensing module 31 can collect environmental channel error source parameters by acquiring error signals output from the magnetic field sensor, temperature sensor, accelerometer, power supply ripple monitor, etc.

[0064] According to the example embodiment, the preset time period can be a future period of time based on the current time. For example, the preset time period can be the time difference between the current moment when the error source parameters are obtained and the propagation of the source waveform (e.g., laser or radio frequency) to the quantum bit. The range of the preset time period can be set from 100 nanoseconds to 2 microseconds. The predicted error parameters can be the predicted error parameters.

[0065] The error fusion and prediction module 32 can determine the prediction error parameters for a preset time period based on the error source parameters through preprocessing (such as bias removal, amplitude-phase decoupling, and unit normalization) and a predictor (such as an MPC model or an ARMA model).

[0066] In step S120, the control system determines the optimized quantum gate parameters based on the prediction error parameters for a preset time period and the preset quantum gate parameters.

[0067] According to the example embodiment, the preset quantum gate parameters can be variable parameters of the target quantum gate operation. The target quantum gate can be the quantum gate applied to the target qubit. The optimized quantum gate parameters can be variable values ​​optimized from the preset quantum gate parameters.

[0068] The gate parameter mapping module 33 can determine the optimized quantum gate parameters based on the prediction error parameters and preset quantum gate parameters for a preset time period.

[0069] For example, the gate parameter mapping module 33 can determine the prediction error vector based on the prediction error parameters for a preset time period, and the gate parameter mapping module 33 can determine the optimized quantum gate parameters based on the prediction error vector, the preset quantum gate parameters, and the compensation matrix parameters.

[0070] In step S130, the control system generates a set of driving waveform parameters for the target qubit corresponding to the optimized quantum gate parameters.

[0071] According to an example embodiment, the target qubit can be the qubit to be manipulated. The driving waveform parameter set can be parameters for generating a driving waveform of a target quantum gate that manipulates the target qubit. The driving waveform parameter set can include the amplitude, phase, frequency, and pulse shape of the driving waveform.

[0072] The waveform generation and execution module 34 can generate a set of driving waveform parameters for the target qubit corresponding to the optimized quantum gate parameters based on the optimized quantum gate parameters.

[0073] In step S140, the control system generates a target quantum gate corresponding to the driving waveform parameter set, so that the target quantum gate can control the target quantum bit.

[0074] According to the example embodiment, the waveform generation and execution module 34 can generate a target quantum gate corresponding to the driving waveform parameter set, so that the target quantum gate can manipulate the target quantum bit.

[0075] The target quantum gate can be generated by a driving waveform. The driving waveform can take many forms. For example, the driving waveform can be laser or microwave.

[0076] For example, when the target quantum gate is generated by laser, the waveform generation and execution module 34 can output the driving waveform through DDS or AWG according to the driving waveform parameter set. After the driving waveform is modulated by AOM or EOM, the target quantum gate is formed, and the target quantum gate manipulates the target quantum bit.

[0077] For example, when the target quantum gate is generated by microwave, the waveform generation and execution module 34 can generate and execute the target quantum gate by outputting the driving waveform through an IQ mixer or a DDS according to the driving waveform parameter set, and the target quantum gate can manipulate the target quantum bit.

[0078] Through the above embodiments, the technical solution of this application can determine the prediction error parameters for a preset time period by acquiring the error source parameters. The technical solution of this application can determine the optimized quantum gate parameters by using the prediction error parameters for the preset time period and the preset quantum gate parameters. The technical solution of this application can generate a set of driving waveform parameters for the target quantum bit corresponding to the optimized quantum gate parameters. The technical solution of this application can generate a target quantum gate corresponding to the driving waveform parameter set, so that the target quantum gate can manipulate the target quantum bit.

[0079] The control method of this application can collect error source parameter information from multiple channels and output error prediction parameters for a preset time period, thereby reducing misjudgments caused by single-channel drift or saturation.

[0080] The control method of this application can access external error source parameter information in real time during the execution of the quantum gate, determine the compensation vector parameters, and distribute them in segments within the quantum gate to achieve gate-level closed-loop suppression.

[0081] The control method of this application combines notch filtering and peak avoidance / servo peak avoidance strategies within the compensation bandwidth, and performs segmented fine-tuning of the quantum gate parameters. This allows for selective suppression of disturbances introduced by servo peaks near the main frequency component of the quantum gate, avoiding systematic phase accumulation errors and over-rotation (or under-rotation) caused by "frequency collision".

[0082] The control method of this application can generate corresponding driving waveforms for manipulating qubits at the hardware level through a set of driving waveform parameters, which is compatible with different driving media and realizes feedforward manipulation of qubits.

[0083] The control method of this application can reduce end-to-end delay and increase compensation bandwidth through the causal link of "error directly driving target quantum gate parameters", and extend the effective compensation bandwidth to cover the main sensitive frequency band of the quantum gate filter function.

[0084] Optionally, see Figure 2 Step S110 may include steps S111-S113.

[0085] In step S111, the control system determines the standardized error source parameters corresponding to the error source parameters based on the error source parameters.

[0086] According to the example embodiment, the standardized error source parameters can be the error signal after standardization. The error sensing module 31 can perform static bias removal, amplitude-phase decoupling, and unit normalization on the error source parameters to generate standardized error source parameters with unified dimensions or unified units.

[0087] For example, error source parameters can be obtained through... It means that, among them, k This is the source channel number. t To obtain the current time of the error source parameters, the standardized error source parameters can be obtained through... express.

[0088] In step S112, the control system determines the time series of the standardized error source based on the standardized error source parameters.

[0089] According to the example embodiment, the standardized error source time series can be a vector generated by sorting the standardized source parameters according to the measurement time. The error sensing module 31 can generate the corresponding standardized error source time series for the standardized error source parameters by sorting them according to the measurement time.

[0090] For example, the standardized error source time series can be determined using the following formula:

[0091] ;

[0092] For standardized error source time series, the representative vector representation is used. , ... Indicates different measurement times; Indicates the time of the first measurement point; This indicates the time of the last measurement point. express Standardized error source parameters for measurement time.

[0093] In step S113, the control system determines the prediction error parameters for a preset time period based on the standardized error source time series.

[0094] According to the example embodiment, the error sensing module 31 can determine the prediction error parameters for a preset time period based on the standardized error source time series through the predictor.

[0095] For example, the predictor can be one of MPC, ARMA, or Bayesian dynamic models, and the predictor can be used to offset the latency of electronics processing and distribution.

[0096] Step S113 can be specifically described as follows: The control system determines the prediction error parameters for a preset time period based on the standardized error source time series and the preset prediction model.

[0097] According to the example embodiment, the preset prediction model can be a preset predictor model. The preset prediction model can correspond to a prediction function.

[0098] For example, the error sensing module 31 can determine the prediction error parameters for a preset time period according to the following formula:

[0099] ;

[0100] in, The prediction error parameter is set for a preset time period; For a preset time period; This is the prediction function.

[0101] Through the above embodiments, the technical solution of this application can determine the prediction error parameters for a preset time period by standardizing and fusing the error source parameters.

[0102] Optionally, see Figure 3 Step S120 may include steps S121-S122.

[0103] In step S121, the control system determines the prediction error vector based on the prediction error parameters for a preset time period.

[0104] According to the example embodiment, the prediction error vector can be in matrix form generated based on the prediction error parameters.

[0105] For example, the gate parameter mapping module 33 can determine the prediction error vector according to the following formula:

[0106] : ;

[0107] in, This is the prediction error vector; express The dimension is N; N is the total number of source channels.

[0108] In step S122, the control system determines the optimized quantum gate parameters based on the prediction error vector, the preset quantum gate parameters, and the compensation matrix parameters.

[0109] According to the example embodiment, the compensation matrix parameters can be the correction values ​​for mapping N error source prediction parameters to L quantum gate parameters.

[0110] The gate parameter mapping module 33 can determine the optimized quantum gate parameters based on the prediction error vector, preset quantum gate parameters, and compensation matrix parameters. For example, the gate parameter mapping module 33 can determine the optimized quantum gate parameters according to the following formula:

[0111] = ;

[0112] in, For the compensation matrix parameters, ; Preset quantum gate parameters; To optimize the quantum gate parameters.

[0113] Through the above embodiments, the technical solution of this application can determine the optimized quantum gate parameters by using the prediction error parameters over a preset time period.

[0114] Optionally, see Figure 4 Step S130 may include steps S131-S132.

[0115] In step S131, the control system determines the driving parameters of the target quantum gate.

[0116] According to the example embodiment, the driving parameters can be parameters for generating the driving waveform. The driving parameters include at least the method for generating the driving waveform and a parameter set corresponding to the method for generating the driving waveform.

[0117] The generation methods for the driving waveform can include laser generation or microwave generation, etc. The parameter set corresponding to the driving waveform generation method can include the amplitude, phase, frequency, and pulse shape of the driving waveform under different driving waveform generation methods.

[0118] For example, the parameter set corresponding to the laser generation method of the driving waveform includes the amplitude of the laser output driving waveform, the phase of the driving waveform, the frequency of the driving waveform, and the pulse shape of the driving waveform.

[0119] The parameter set for generating the microwave driving waveform includes the amplitude, phase, frequency, and pulse shape of the driving waveform output by the microwave.

[0120] The waveform generation and execution module 34 can determine the driving parameters of the target quantum gate based on the source end of the driving waveform.

[0121] In step S132, the control system determines the set of driving waveform parameters based on the optimized quantum gate parameters and the driving parameters of the target quantum gate.

[0122] According to the example embodiment, the waveform generation and execution module 34 can generate a hardware-level driving waveform parameter set based on the optimized quantum gate parameters and the driving parameters of the target quantum gate.

[0123] For example, when a quantum gate is generated by laser, the waveform generation and execution module 34 can determine the driving waveform parameter set based on the optimized quantum gate parameters and the parameter set corresponding to the laser generation method of the driving waveform.

[0124] Through the above embodiments, the technical solution of this application can determine the driving waveform parameter set by optimizing the quantum gate parameters and the driving parameters of the target quantum gate, and can generate the corresponding driving waveform at the hardware level.

[0125] Optionally, see Figure 7 The control system may also include a monitoring and acceptance module 35.

[0126] The following is combined with Figure 7 This application describes a control method 2000 for feedforward optimization of quantum gate parameters. See also: Figure 5 The control method 2000 may include steps S210-S250.

[0127] See Figure 5 Steps S210-S240 are the same as steps S110-S140, so they will not be described again.

[0128] In step S250, the control system determines the evaluation parameters of the target quantum gate based on the actual error parameters of the target quantum gate after execution, so as to update the compensation matrix parameters according to the evaluation parameters.

[0129] According to the example embodiment, the actual error parameter can be the performance error signal of the target quantum gate. The monitoring and acceptance module 35 can monitor the target quantum gate in real time and collect the actual error parameter of the target quantum gate at fixed intervals.

[0130] Evaluation parameters can be indices used to evaluate the operation of a target quantum gate. For example, evaluation parameters may include gate drift parameters.

[0131] The monitoring and acceptance module 35 can determine the evaluation parameters of the target quantum gate based on the actual error parameters of the target quantum gate after execution, and update the compensation matrix parameters based on the evaluation parameters.

[0132] The compensation matrix parameters can be obtained through calibration, or they can be adjusted over time using an adaptive update method based on the evaluation parameters to adapt to different operating points or noise conditions.

[0133] Through the above embodiments, the technical solution of this application can determine the evaluation parameters of the quantum gate through the actual error parameters, thereby updating the compensation matrix parameters.

[0134] The control method of this application can monitor and verify the target quantum gate after optimization in real time, forming a basis for closed-loop self-tuning and version rollback.

[0135] According to another aspect of this application, this application also provides a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is capable of implementing the control method for quantum gate parameter feedforward optimization as described above.

[0136] According to another aspect of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the control method for quantum gate parameter feedforward optimization as described above.

[0137] According to another aspect of this application, this application also provides a computer program product, comprising: a computer program stored on a computer-readable storage medium; the computer program includes program instructions that, when executed by a computer, cause the computer to perform the control method for quantum gate parameter feedforward optimization as described above.

[0138] Finally, it should be noted that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions of the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A control method for feedforward optimization of quantum gate parameters, characterized in that, The control method includes: Based on the obtained error source parameters, determine the prediction error parameters for the preset time period; Based on the prediction error parameters and preset quantum gate parameters for the preset time period, the optimized quantum gate parameters are determined. Based on the optimized quantum gate parameters, a set of driving waveform parameters for the target qubit corresponding to the optimized quantum gate parameters is generated; Based on the driving waveform parameter set, a target quantum gate corresponding to the driving waveform parameter set is generated so that the target quantum gate can control the target quantum bit.

2. The control method according to claim 1, characterized in that, The step of determining the prediction error parameters for a preset time period based on the acquired error source parameters includes: Based on the error source parameters, determine the standardized error source parameters corresponding to the error source parameters; Based on the standardized error source parameters, determine the standardized error source time series; Based on the standardized error source time series, the prediction error parameters for the preset time period are determined.

3. The control method according to claim 2, characterized in that, The step of determining the prediction error parameters for the preset time period based on the standardized error source time series includes: Based on the standardized error source time series and the preset prediction model, the prediction error parameters for the preset time period are determined.

4. The control method according to claim 1, characterized in that, Based on the prediction error parameters for the preset time period and the preset quantum gate parameters, the optimized quantum gate parameters are determined, including: The prediction error vector is determined based on the prediction error parameters for the preset time period; The optimized quantum gate parameters are determined based on the prediction error vector, the preset quantum gate parameters, and the compensation matrix parameters.

5. The control method according to claim 4, characterized in that, After generating a target quantum gate corresponding to the driving waveform parameter set based on the driving waveform parameter set, so that the target quantum gate manipulates the target qubit, the control method further includes: Based on the actual error parameters of the target quantum gate after execution, the evaluation parameters of the target quantum gate are determined, and the compensation matrix parameters are updated according to the evaluation parameters.

6. A control system for quantum gate parameter feedforward optimization, characterized in that, The control system includes: The error sensing module acquires error source parameters; The error fusion and prediction module determines the prediction error parameters for a preset time period based on the error source parameters. The gate parameter mapping module determines the optimized quantum gate parameters based on the prediction error parameters and preset quantum gate parameters for the preset time period. The waveform generation and execution module generates a set of driving waveform parameters for the target qubit corresponding to the optimized quantum gate parameters based on the optimized quantum gate parameters. The waveform generation and execution module also generates a target quantum gate corresponding to the driving waveform parameter set based on the driving waveform parameter set, so that the target quantum gate controls the target qubit.

7. The control system according to claim 6, characterized in that, The error fusion and prediction module determines the standardized error source parameters corresponding to the error source parameters based on the error source parameters. The error fusion and prediction module determines the standardized error source time series based on the standardized error source parameters; The error fusion and prediction module determines the prediction error parameters for the preset time period based on the standardized error source time series.

8. The control system according to claim 7, characterized in that, The error fusion and prediction module determines the prediction error parameters for the preset time period based on the standardized error source time series and the preset prediction model.

9. The control system according to claim 6, characterized in that, The gate parameter mapping module determines the prediction error vector based on the prediction error parameters of the preset time period; The gate parameter mapping module determines the optimized quantum gate parameters based on the prediction error vector, the preset quantum gate parameters, and the compensation matrix parameters.

10. The control system according to claim 9, characterized in that, The control system further includes: The monitoring and acceptance module determines the evaluation parameters of the target quantum gate based on the actual error parameters of the target quantum gate after execution, and updates the compensation matrix parameters based on the evaluation parameters.

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