The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control.
The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control.
Achieving precise control over quantum systems has groundbreaking ap-plications, such as simulating complex quantum systems, or implementing quantum algorithms with an improved efficiency over classical algorithms. Quantum computing has been under development for the last decades, re-sulting in the so-called Noisy Intermediate-Scale Quantum (NISQ) devices with about 50 qubits. While these devices are built with the purpose of achieving practical advantages over classical processors, they are vulnerable to noise sources. In order to account for error sources, one needs to im-plement Quantum Error Correction (QEC) techniques. These techniques protect quantum information by encoding a logical qubit into several phys-ical ones. For the implementation of NISQ devices and QEC techniques, it is vital to keep improving the quantum operations and states involved. Here we develop calibration protocols based on the application of Bayesian inference. We show their advantages compared to the more traditional frequentist approaches, such as their ability to maximise the information gained with each measurement, or their straightforward automation. In our first line of research, we develop a Bayesian protocol for the correction of unwanted phases appearing in the experimental preparation of Steane code states. This protocol requires 13 times less measurements than the frequentist approach. In our second line of research, we develop a proto-col for locking a laser to the qubit transition frequency in a trapped-ion architecture. This calibration is vital, since the correct implementation of single-qubit gates depends on it. In our final line of research, we study the Mølmer-Sørensen entangling gate, used in trapped-ion systems. First we introduce the calibration parameters, and derive a semianalytical model of the effects of one of them, for which there was no previous analytical understanding. Finally, we develop a Bayesian protocol for the calibration of these parameters, which was successfully implemented and tested in an experimental trapped-ion quantum processor.
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.
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/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.
Quantum hardware control -- the precise calibration of microwave pulses, flux biases, and laser parameters that drive qubit operations -- is a critical bottleneck in scaling superconducting and trapped-ion quantum processors. Manual calibration is time-consuming, requires expert domain knowledge, and fails to adapt to rapid parameter drift between calibration cycles. AI-assisted control systems offer automated, adaptive alternatives using reinforcement learning (RL), Bayesian optimisation (BO), and neural network surrogate models to continuously optimise hardware control parameters. This paper proposes the AI Quantum Control Framework (AQCF), evaluating six AI control strategies across five hardware optimisation tasks: single-qubit gate calibration, two-qubit gate optimisation, crosstalk suppression, readout fidelity maximisation, and real-time drift compensation. AQCF is evaluated on IBM Quantum Eagle and IonQ Aria hardware over 60-day continuous operation. Key results: RL-based pulse optimisation improves two-qubit gate fidelity by 18.4% over manual calibration; Bayesian optimisation reduces calibration time by 84% (from 4.2 hours to 40 minutes); neural surrogate models enable real-time drift compensation with 12.8-minute response latency. The framework introduces the AI Control Efficiency Score (ACES) and a deployment roadmap for autonomous quantum hardware management.
Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity quantum gates particularly challenging. We propose a hybrid optimization framework that integrates optimal control theory methods with contextual deep reinforcement learning to achieve robust controlled-phase gates on two qutrits. Optimal control is first used to design high-fidelity control pulses for a nominal system model. Reinforcement learning is then employed as a calibration stage that learns small residual corrections to these pulses in the presence of static model mismatch, thereby preserving good gate performance under realistic parameter uncertainties. By learning structured, low-dimensional residual corrections conditioned on device-specific parameter variations, reinforcement learning enhances the transfer robustness of nominally optimal but parameter-sensitive control solutions across ensembles of devices. Crucially, the reinforcement learning step in our framework does not compete with the optimal control step but provides the adaptability required for realistic hardware, systematically reducing the sensitivity to parameter fluctuations. Our results establish reinforcement learning as a practical and scalable ingredient for robust calibration of quantum gates in high-dimensional systems.