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.
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.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.
The Control Variational Quantum Eigensolver (ctrl-VQE) directly optimizes microwave pulses to enable faster and lower-error quantum-state preparation, but its continuous control landscape re- quires efficient search strategies. We demonstrate that a reinforcement-learning agent based on a deep Q learning network can autonomously discover high-performance pulse sequences using only system parameters and a reward function. The approach is fully general for superconducting qubit platforms, requires no ansatz, and operates at nanosecond resolution compatible with hardware con- straints. As a proof of concept, we apply the method to ground-state preparation of the Hydrogen molecule on a simulated superconducting device. The agent consistently identifies optimized control sequences that achieve high fidelity and outperform random-search baselines. These results highlight adaptive learning as a promising hardware-ready framework for pulse-level quantum control.
The growing scale and complexity of photonic quantum systems demand a transition from manual, heuristic control toward intelligent, autonomous operational frameworks. Quantum Artificial Intelligence (QAI) integrates machine learning, quantum-enhanced optimization, and real-time feedback to address key challenges in the calibration, stabilization, and scaling of photonic quantum technologies. Unlike conventional machine-learning-assisted quantum control and self-driving laboratory approaches that rely primarily on classical intelligence, the proposed QAI paradigm establishes a tighter integration between artificial intelligence and quantum hardware. Parameterized quantum circuits executed directly on the photonic processor participate in the agent’s decision-making process, while the same framework supports both real-time control tasks. QAI combines variational quantum neural networks, meta-reinforcement learning, multi-agent control architectures, and quantum optimization methods, such as the Quantum Approximate Optimization Algorithm, to enable adaptive calibration, error mitigation, and high-dimensional optimization in programmable photonic platforms. While this perspective focuses on real-time control and calibration, we also outline a conceptual pathway toward autonomous generative inverse design. To guide future development, we propose a three-stage roadmap that progresses from open-source hybrid quantum–classical control tools to fully autonomous laboratories capable of closed-loop self-optimization and experiment design. Experimental platforms include programmable silicon photonic circuits and lithium-niobate interferometers, where thermal drift, fabrication variability, and crosstalk require intelligent adaptation. Beyond quantum optics, the QAI framework has broader implications for quantum sensing, secure communications, and photonic computing. By introducing a unified QAI framework and a structured classification of Quantum Autonomy Levels (QALs), this work provides a roadmap toward autonomous quantum-optics laboratories and photonic quantum technologies.
Developing scalable, fault-tolerant atomic quantum processors requires precise control over large arrays of optical beams. This remains a major challenge due to inherent imperfections in classical control hardware, such as inter-channel crosstalk and beam leakage. In this work, we introduce a hardware co-designed intelligent quantum control framework to address these limitations. We construct a mathematical model of the photonic control hardware, integrate it into the quantum optimal control (QOC) framework, and apply reinforcement learning (RL) techniques to discover optimal control strategies. We demonstrate that the proposed framework enables robust, high-fidelity parallel single-qubit gate operations under realistic control conditions, where each atom is individually addressed by an optical beam. Specifically, we implement and benchmark three optimization strategies: a classical hybrid Self-Adaptive Differential Evolution-Adam (SADE-Adam) optimizer, a conventional RL approach based on Proximal Policy Optimization (PPO), and a novel end-to-end differentiable RL method. Using SADE-Adam as a baseline, we find that while PPO performance degrades as system complexity increases, the end-to-end differentiable RL consistently achieves gate fidelities above 99.9%, exhibits faster convergence, and maintains robustness under varied channel crosstalk strength and randomized dynamic control imperfections.
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.
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.