Control of quantum mechanical systems through optimisation

Pãnos M. Pardalos, Vitaliy A. Yatsenko

Alexander Pitchford, Andrey A. Rakhubovsky, Rick Mukherjee +5 more

Nonclassical correlations provide a resource for many applications in quantum technology as well as providing strong evidence that a system is indeed operating in the quantum regime. Optomechanical systems can be arranged to generate quantum entanglement between the mechanics and a mode of travelling light. Here we propose automated optimisation of the production of quantum correlations in such a system, beyond what can be achieved through analytical methods, by applying Bayesian optimisation to the control parameters. Two-mode optomechanical squeezing experiment is simulated using a detailed theoretical model of the system, while the Bayesian optimisation process modifies the controllable parameters in order to maximise the non-classical two-mode squeezing and its detection, independently of the inner workings of the model. The Bayesian optimisation treats the simulations or the experiments as a black box. This we refer to as \emph{theory-blind} optimisation, and the optimisation process is designed to be unaware of whether it is working with a simulation or the actual experimental setup. We find that in the experimentally relevant thermal regimes, the ability to vary and optimise a broad array of control parameters provides access to large values of two-mode squeezing that would otherwise be difficult or intractable to discover. In particular we observe that modulation of the driving frequency around the resonant sideband, when added to the set of control parameters, produces strong nonclassical correlations greater on average than the maximum achieved by optimising over the remaining parameters. We also find that using our optimisation approach raises the upper limit to the thermal regime in which squeezing can be achieved. This extends the range of experimental setups in which non-classical correlations could be generated beyond the region of high quantum cooperativity.

Anatoliy G. Butkovskiy, Yu. I. Samoilenko

Alexander

The scaling of effort to achieve control objectives with system size is an important consideration in the development of many quantum devices, especially in those intended for quantum information processing. This thesis investigates the scaling with system size of effort required to control manybody quantum systems, and asks whether this is sufficiently favourable to enable supremacy in quantum computation. This is tested using quantum control theory and numerical control optimisation, which are well established tools for investigating the dynamics of driven quantum systems, in a variety of theoretical models related to state-of-the-art quantum devices. Gates acting on a chain of coupled oscillators are found to achievable in times that scale approximately linear with chain length, where the control is over only a single oscillator. The scalability of a scheme for implementing quantum gates in a many-body quantum simulator is shown to be favourable enough to allow for them to be configured to perform quantum computations. A state measurement protocol is proposed as part of the scheme. The scalability is validated through numerical simulation of simulator models composed of up to 9 qubits. The reachable set of operations for systems with quadratic Hamiltonians and infinite-dimensional Hilbert spaces is investigated. The passive operations in quantum optics are proven to be unreachable for single-mode systems with ‘unstable’ Hamiltonians. Further characterisation of the reachable set is made through numerical simulation of control optimisation. Some progress is made in extending the reachability result to n-modes. A non-Markovian noise model is used in simulating control optimisation of a dynamical decoupling scheme, which would be impossible to simulate with Markovian dynamics. The popular QuTiP Python library has been extended to allow simulation of control optimisation with a range of dynamical models. A description of these software modules and their method of use is given. Many of the software tools developed for the study are made available through open-source repositories. Some outlook is given for the use of in-situ open-loop control in optimising controls in quantum system experiments.

J. W. Clark, Dennis G. Lucarelli, Tzyh‐Jong Tarn

Clara Schmidt, Isabella Rossi

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.