We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipshitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipshitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.

Feb 1, 20190 citations

Bayesian ACRONYM Tuning

John King Gamble, Christopher Granade, Nathan Wiebe

We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.

Leibniz international proceedings in informaticsJan 1, 20190 citations

Fast Quantum Calibration using Bayesian Optimization with State Parameter Estimator for Non-Markovian Environment

Qian, Peng, Qamar, Shahid, Xiao Xiao +5 more

As quantum systems expand in size and complexity, manual qubit characterization and gate optimization will be a non-scalable and time-consuming venture. Physical qubits have to be carefully calibrated because quantum processors are very sensitive to the external environment, with control hardware parameters slowly drifting during operation, affecting gate fidelity. Currently, existing calibration techniques require complex and lengthy measurements to independently control the different parameters of each gate and are unscalable to large quantum systems. Therefore, fully automated protocols with the desired functionalities are required to speed up the calibration process. This paper aims to propose single-qubit calibration of superconducting qubits under continuous weak measurements from a real physical experimental settings point of view. We propose a real-time optimal estimator of qubit states, which utilizes weak measurements and Bayesian optimization to find the optimal control pulses for gate design. Our numerical results demonstrate a significant reduction in the calibration process, obtaining a high gate fidelity. Using the proposed estimator we estimated the qubit state with and without measurement noise and the estimation error between the qubit state and the estimator state is less than 0.02. With this setup, we drive an approximated pi pulse with final fidelity of 0.9928. This shows that our proposed strategy is robust against the presence of measurement and environmental noise and can also be applicable for the calibration of many other quantum computation technologies.As quantum systems expand in size and complexity, manual qubit characterization and gate optimization will be a non-scalable and time-consuming venture. Physical qubits have to be carefully calibrated because quantum processors are very sensitive to the external environment, with control hardware parameters slowly drifting during operation, affecting gate fidelity. Currently, existing calibration techniques require complex and lengthy measurements to independently control the different parameters of each gate and are unscalable to large quantum systems. Therefore, fully automated protocols with the desired functionalities are required to speed up the calibration process. This paper aims to propose single-qubit calibration of superconducting qubits under continuous weak measurements from a real physical experimental settings point of view. We propose a real-time optimal estimator of qubit states, which utilizes weak measurements and Bayesian optimization to find the optimal control pulses for gate design. Our numerical results demonstrate a significant reduction in the calibration process, obtaining a high gate fidelity. Using the proposed estimator we estimated the qubit state with and without measurement noise and the estimation error between the qubit state and the estimator state is less than 0.02. With this setup, we drive an approximated pi pulse with final fidelity of 0.9928. This shows that our proposed strategy is robust against the presence of measurement and environmental noise and can also be applicable for the calibration of many other quantum computation technologies.

arXiv:2205.12929May 25, 20221 citation

Experimental Bayesian Calibration of Trapped-Ion Entangling Operations

Lukas Gerster, Fernando Martínez-García, Pavel Hrmo +7 more

The performance of quantum gate operations is experimentally determined by how correctly operational parameters can be determined and set, and how stable these parameters can be maintained.In addition, gates acting on different sets of qubits require unique sets of control parameters.Thus, an efficient multidimensional parameter estimation procedure is crucial to calibrate even medium-sized quantum processors.Here, we develop and characterize an efficient calibration protocol to automatically estimate and adjust experimental parameters of the widely used two-qubit Mølmer-Sørensen entangling gate operation in a trapped-ion quantum information processor.The protocol exploits Bayesian parameter estimation methods that include a stopping criterion based on a desired gate infidelity.We experimentally demonstrate a tune-up procedure that leads to a residual median gate infidelity due to miscalibration of 1.3(1) × 10 -3 , requiring 1200 ± 500 experimental cycles, while completing the entire gate calibration procedure in less than one minute, which provides a significant speedup over commonly used manual tune-up routines.This approach is applicable to other quantum information processor architectures with known or sufficiently characterized theoretical models.The performance of quantum gate operations is experimentally determined by how correctly operational parameters can be determined and set, and how stable these parameters can be maintained.In addition, gates acting on different sets of qubits require unique sets of control parameters.Thus, an efficient multidimensional parameter estimation procedure is crucial to calibrate even medium-sized quantum processors.Here, we develop and characterize an efficient calibration protocol to automatically estimate and adjust experimental parameters of the widely used two-qubit Mølmer-Sørensen entangling gate operation in a trapped-ion quantum information processor.The protocol exploits Bayesian parameter estimation methods that include a stopping criterion based on a desired gate infidelity.We experimentally demonstrate a tune-up procedure that leads to a residual median gate infidelity due to miscalibration of 1.3(1) × 10 -3 , requiring 1200 ± 500 experimental cycles, while completing the entire gate calibration procedure in less than one minute, which provides a significant speedup over commonly used manual tune-up routines.This approach is applicable to other quantum information processor architectures with known or sufficiently characterized theoretical models.

arXiv:2112.01411PRX QuantumJun 8, 202224 citations

Quantum Machine Learning: A Bayesian Optimization-Based Algorithm for Automated Model Parameter Tuning

Jincheng Zhang

This paper introduces a novel quantum machine learning algorithm based on Bayesian optimization, designed to automatically adjust model parameters for optimal performance. Traditional machine learning model parameter optimization often relies on manual adjustments, which struggles to adapt to evolving data. Our algorithm leverages the probabilistic nature of model parameters, employing Bayesian optimization to dynamically refine the model, ultimately leading to improved accuracy and generalization. This work presents a fundamentally new approach to model parameter tuning, offering enhanced adaptability and potential for superior results compared to existing methods.This paper introduces a novel quantum machine learning algorithm based on Bayesian optimization, designed to automatically adjust model parameters for optimal performance. Traditional machine learning model parameter optimization often relies on manual adjustments, which struggles to adapt to evolving data. Our algorithm leverages the probabilistic nature of model parameters, employing Bayesian optimization to dynamically refine the model, ultimately leading to improved accuracy and generalization. This work presents a fundamentally new approach to model parameter tuning, offering enhanced adaptability and potential for superior results compared to existing methods.

Sep 4, 20260 citations

Real-Time DCZ Gate Control by Bayesian Optimization with Forgetting

Yudai Sato, T. Kawahara

Silicon spin qubits are a promising platform for large-scale quantum computing because of their compatibility with advanced semiconductor fabrication technology. However, charge noise remains a major challenge since it causes timedependent drifts in qubit frequency and exchange interactions through electric field fluctuations. These drifts in control parameters are one obstacle to achieving the high-fidelity quantum operations required for quantum error correction. In this work, we propose a Bayesian optimization (BO) framework for the adaptive calibration of quantum gate parameters in silicon qubits. In addition, to address the impact of charge noise, which exhibits a $1 / f$ -like power spectral density, we incorporate a forgetting mechanism into the Gaussian process model used during optimization. Our simulations on a realistic silicon quantum dot system show that BO-based method improves gate fidelity by 0.266% and reduces calibration iterations by 36.9% compared to random search. Compared to standard BO, our modified method achieves a further fidelity gain of 0.008% and a 9.23% reduction in iterations. These results highlight the potential of BO-based calibration for robust quantum gate implementation under realistic noise conditions.Silicon spin qubits are a promising platform for large-scale quantum computing because of their compatibility with advanced semiconductor fabrication technology. However, charge noise remains a major challenge since it causes timedependent drifts in qubit frequency and exchange interactions through electric field fluctuations. These drifts in control parameters are one obstacle to achieving the high-fidelity quantum operations required for quantum error correction. In this work, we propose a Bayesian optimization (BO) framework for the adaptive calibration of quantum gate parameters in silicon qubits. In addition, to address the impact of charge noise, which exhibits a$1 / f$-like power spectral density, we incorporate a forgetting mechanism into the Gaussian process model used during optimization. Our simulations on a realistic silicon quantum dot system show that BO-based method improves gate fidelity by 0.266% and reduces calibration iterations by 36.9% compared to random search. Compared to standard BO, our modified method achieves a further fidelity gain of 0.008% and a 9.23% reduction in iterations. These results highlight the potential of BO-based calibration for robust quantum gate implementation under realistic noise conditions.

IEEE International Conference on Quantum Computing and Engineering (QCE)Aug 30, 20250 citations

1 / f

-like power spectral density, we incorporate a forgetting mechanism into the Gaussian process model used during optimization. Our simulations on a realistic silicon quantum dot system show that BO-based method improves gate fidelity by 0.266% and reduces calibration iterations by 36.9% compared to random search. Compared to standard BO, our modified method achieves a further fidelity gain of 0.008% and a 9.23% reduction in iterations. These results highlight the potential of BO-based calibration for robust quantum gate implementation under realistic noise conditions.