Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2205.12929

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

Qian, Peng, Qamar, Shahid, Xiao Xiao, Yanwu Gu, Xudan Chai, Zhen Wei Zhao, Nicolò Forcellini, Dong E. Liu

Published May 25, 2022• 1 citation
arXivHTMLPDF

Abstract

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.

DOI
10.48550/arxiv.2205.12929

Related papers

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.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Aug 30, 2025•0 citations

Automatic Qubit Characterization and Gate Optimization with QubiC

Yilun Xu, Gang Huang, J. T. Balewski +6 more

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose mutli-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of $4.9(1.1) \times 10^{-4}$ and $1.4(3) \times 10^{-2}$, respectively.

arXiv:2104.10866•Apr 22, 2021•5 citations

Automatic Qubit Characterization and Gate Optimization with QubiC

Yilun Xu, Gang Huang, J. T. Balewski +6 more

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC , which is an open source FPGA (field-programmable gate array)-based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.

ACM Transactions on Quantum Computing•Apr 13, 2022•11 citations

Few-Shot, Robust Calibration of Single Qubit Gates Using Bayesian Robust Phase Estimation

Travis Hurant, Ke Sun, Zhubing Jia +2 more

Accurate calibration of control parameters in quan-tum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase Estimation (RPE) [8] has emerged as a practical and effective calibration technique aimed at tackling this challenge. It combines a provably efficient number of control pulses with a classical post-processing algorithm to estimate the phase accumulated by a quantum gate. We introduce Bayesian Robust Phase Estimation (BRPE), an in-novative approach that integrates Bayesian parameter estimation into the classical post-processing phase to reduce the sampling overhead. Our numerical analysis shows that BRPE markedly reduces phase estimation errors, requiring approximately 50% fewer samples than standard RPE. Specifically, in an ideal, noise-free setting, it achieves up to a 96% reduction in average absolute estimation error for a fixed sample cost of 88 shots when compared to RPE. Under a depolarizing noise model, it attains up to a 47% reduction for a fixed cost of 1 76 shots. Additionally, we adapt BRPE for Ramsey spectroscopy applications and successfully implement it experimentally in a trapped ion system.

arXiv:2407.18339•IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•0 citations

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.

arXiv:2112.01411•PRX Quantum•Jun 8, 2022•24 citations

Leakage reduction in fast superconducting qubit gates via optimal control

M. Werninghaus, D. J. Egger, F. Roy +3 more

Abstract Reaching high-speed, high-fidelity qubit operations requires precise control over the shape of the underlying pulses. For weakly anharmonic systems, such as superconducting transmon qubits, short gates lead to leakage to states outside of the computational subspace. Control pulses designed with open-loop optimal control may reduce such leakage. However, model inaccuracies can severely limit the usability of such pulses. We implemented a closed-loop optimization that simultaneously adapts all control parameters based on measurements of a cost function built from Clifford gates. We directly optimize the amplitude and phase of each sample point of the digitized control pulse. We thereby fully exploit the capabilities of the pulse generation electronics and create a 4.16 ns single-qubit pulse with 99.76 % fidelity and 0.044 % leakage. This is a sevenfold reduction of the leakage rate and a threefold reduction in standard errors of the best DRAG pulse we have calibrated at such short durations on the same system.

arXiv:2003.05952•npj Quantum Information•Jan 29, 2021•192 citations
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.
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 a1/f1 / f1/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.
As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose mutli-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1)×10−44.9(1.1) \times 10^{-4}4.9(1.1)×10−4 and 1.4(3)×10−21.4(3) \times 10^{-2}1.4(3)×10−2, respectively.
As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC , which is an open source FPGA (field-programmable gate array)-based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.
Accurate calibration of control parameters in quan-tum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase Estimation (RPE) [8] has emerged as a practical and effective calibration technique aimed at tackling this challenge. It combines a provably efficient number of control pulses with a classical post-processing algorithm to estimate the phase accumulated by a quantum gate. We introduce Bayesian Robust Phase Estimation (BRPE), an in-novative approach that integrates Bayesian parameter estimation into the classical post-processing phase to reduce the sampling overhead. Our numerical analysis shows that BRPE markedly reduces phase estimation errors, requiring approximately 50% fewer samples than standard RPE. Specifically, in an ideal, noise-free setting, it achieves up to a 96% reduction in average absolute estimation error for a fixed sample cost of 88 shots when compared to RPE. Under a depolarizing noise model, it attains up to a 47% reduction for a fixed cost of 1 76 shots. Additionally, we adapt BRPE for Ramsey spectroscopy applications and successfully implement it experimentally in a trapped ion system.
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.
Abstract Reaching high-speed, high-fidelity qubit operations requires precise control over the shape of the underlying pulses. For weakly anharmonic systems, such as superconducting transmon qubits, short gates lead to leakage to states outside of the computational subspace. Control pulses designed with open-loop optimal control may reduce such leakage. However, model inaccuracies can severely limit the usability of such pulses. We implemented a closed-loop optimization that simultaneously adapts all control parameters based on measurements of a cost function built from Clifford gates. We directly optimize the amplitude and phase of each sample point of the digitized control pulse. We thereby fully exploit the capabilities of the pulse generation electronics and create a 4.16 ns single-qubit pulse with 99.76 % fidelity and 0.044 % leakage. This is a sevenfold reduction of the leakage rate and a threefold reduction in standard errors of the best DRAG pulse we have calibrated at such short durations on the same system.