In this work the application of machine learning methods for quantum control of trapped ion quantum computers is studied. This is done in the framework of a single quantum gate, the geometric phase gate. The fundamentals of trapping ions as well as machine learning are presented. The Hamiltoninan operator is derived for a system of two ions in a linear Paul trap. After presenting the consepts of open quantum systems and quantum computing in general, a master equation for two ions in a noisy environment that are driven by a laser is presented. The solution to the master equation is used to generate datasets that corresond to the application of a geometric phase gate. The datasets are then used to train a variety of machine learning models that emulate the mapping from the shape of the control laser all the way to the resulting fidelity of the gate operation. The output of the models is then optimised to acquire the optimal control parameters describing the laser pulse.
In this work the application of machine learning methods for quantum control of trapped ion quantum computers is studied. This is done in the framework of a single quantum gate, the geometric phase gate. The fundamentals of trapping ions as well as machine learning are presented. The Hamiltoninan operator is derived for a system of two ions in a linear Paul trap. After presenting the consepts of open quantum systems and quantum computing in general, a master equation for two ions in a noisy environment that are driven by a laser is presented. The solution to the master equation is used to generate datasets that corresond to the application of a geometric phase gate. The datasets are then used to train a variety of machine learning models that emulate the mapping from the shape of the control laser all the way to the resulting fidelity of the gate operation. The output of the models is then optimised to acquire the optimal control parameters describing the laser pulse.
State-of-the-art two-qubit gates with trapped ions employ open-loop control that rely on simplified models to precompute control sequences. Our aim is to introduce closed-loop control for two-qubit gates to correct disturbances as they occur during the gate implementation. We introduce a spectator ion into the ion chain used for quantum logic processing, where it couples with the other ions through collective motional modes. The spectator ion's position is continuously monitored by driving dipole transitions and detecting the resultant fluorescence. We show that incorporating a spectator ion is feasible for linear Paul trap implementations and is expected to reduce the two-qubit gate Bell-state preparation infidelity by an order of magnitude with the deleterious effects of position monitoring being negligible compared to the thermal effects that exist in the system, even in the absence of the spectator ions. Mathematically, we describe driven ion-trap dynamics, including the spectator ion, by a stochastic quantum master equation involving the amplitude-modulation multimode-motional coupling gate, motional drift, thermal effects, recoil from photon scattering, spontaneous decay, and light shift. Our on-the-fly control method employs reinforcement learning with the reward function based on the actual geometric phase of the spectator ion. A key advantage of our approach is that we introduce a control method that involves `learning' and correcting disturbances happening in the trap on-the-fly, thus achieving high-fidelity gates. Our approach will lead to a significantly higher two-qubit gate fidelity at a reduced calibration overhead owing to the small parameter drift in the control system.
Quantum control plays an irreplaceable role in practical use of quantum computers. However, some challenges have to be overcome to find more suitable and diverse control parameters. We propose a promising and generalizable average-fidelity-based machine-learning-inspired method to optimize the control parameters, in which a neural network with periodic feature enhancement is used as an ansatz. In the implementation of a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering, compared with the control parameters in the simple form of a trigonometric function, our approach can yield significantly higher-fidelity (>99.99 phase gates, such as the $\ensuremath{\pi}/8$ gate (t gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise, and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multiqubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.
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.
Machine learning brings the promise of deriving meaning from different data, which is widely used for medicine and computer science. Quantum computing exploits the phenomena of quantum states to realize parallel computing, which believed to be significantly faster than classical computers. Quantum machine learning is the integration of quantum algorithms within machine learning programs, thereby improving classical machine learning techniques. Quantum control such as gate decomposition and Hamiltonian control is an essential problem to solve in quantum dynamics. Here we present a hybrid quantum machine learning model to conduct quantum control optimization. Firstly, according to Bloch theorem, any single-qubit unitary gate can be specified by three vector rotations. We find the optimal decomposition of the given gate, which is the optimal control sequence. Secondly, we build a hybrid quantum-classical neural networks to solve gate decomposition problem utilizing supervised learning. And we prepare the training data and train the hybrid supervised model. Finally, we demonstrate the quantum control optimization results in hybrid model on quantum gates.
Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations on a superconducting transmon qubit, using detailed numerical simulations. We demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of about 99.99%.
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.