Reinforcement Learning Optimises Quantum Control Amidst System Uncertainties and Noise.

Researchers demonstrate reinforcement learning, specifically the soft actor-critic algorithm, effectively optimises control of a spin-based magnetometer. The trained agent determines pulse sequences to maximise magnetic field magnitude precision, exhibiting good generalisation across unseen Hamiltonian parameters, though performance is sensitive to pulse duration and initial state purity.

The pursuit of enhanced precision in magnetic field measurement underpins numerous technologies, from medical imaging to materials science. Achieving optimal sensitivity in these systems, particularly when faced with environmental disturbances known as decoherence and incomplete knowledge of system characteristics, presents a significant challenge. Researchers are now exploring the application of artificial intelligence, specifically reinforcement learning, to navigate these complexities and refine control strategies. A team led by Logan W. Cooke and Stefanie Czischek, both from the Department of Physics at the University of Ottawa, details their investigation into this approach in a recent publication titled “Reinforcement Learning for Optimal Control of Spin Magnetometers”. Their work utilises the soft actor-critic (SAC) algorithm, a model-free reinforcement learning technique, to optimise the performance of a spin-based magnetometer —a device that measures magnetic fields by observing the behaviour of atomic spins. This demonstrates the potential for intelligent control in complex quantum systems.

Researchers demonstrate the effective application of reinforcement learning, specifically the soft actor-critic (SAC) algorithm, to optimise quantum control, achieving enhanced precision in estimating background magnetic field magnitudes. The study successfully trains a reinforcement learning (RL) agent to design pulse sequences for a spin-based magnetometer, representing a development towards automating the design of control strategies, particularly where analytical solutions are difficult to obtain. Traditional methods often rely on pre-defined control strategies, but this approach allows the agent to learn optimal control directly from the system’s dynamics and adapt to complex quantum systems.

The findings indicate the trained agent effectively generalises to different quantum sensors, enhancing the practicality of this approach and expanding its applicability. Researchers are actively exploring the potential of combining reinforcement learning with other machine learning techniques, such as Bayesian optimisation, to accelerate the learning process and improve the efficiency of control strategy design. Bayesian optimisation is a probabilistic approach used to find the optimum of an objective function, particularly useful when evaluating the function is expensive, as is often the case in quantum experiments.

The research highlights the utility of machine learning techniques in addressing the complexities of quantum metrology, framing the control problem as a reinforcement learning task and bypassing the need for explicit analytical modelling. This allows the agent to discover effective control strategies through interaction with a simulated quantum system, proving particularly valuable when dealing with multi-parameter estimation. Scientists leverage tools such as the Julia programming language, alongside packages like DifferentialEquations.jl and PyTorch, for efficient numerical computation and machine learning implementation, streamlining the development process.

Researchers actively investigate the use of more advanced reward functions to guide the learning process, improving the performance of the RL agent and optimising the control strategies. They explore the potential of using hierarchical reinforcement learning to break down complex tasks into smaller, more manageable sub-tasks, simplifying the learning process and enhancing the efficiency of the system. Scientists also investigate the use of transfer learning to leverage knowledge gained from previous tasks, accelerating the learning process and improving the performance of the agent. Transfer learning allows an agent to apply knowledge gained while solving one problem to a different but related problem.

Future work should focus on extending this framework to more complex quantum systems, exploring the use of alternative RL algorithms and investigating the potential of combining reinforcement learning with other machine learning techniques. Incorporating noise models more realistically reflecting experimental conditions will assess the robustness of the learned control policies in real-world scenarios and ensure their reliability.

The research also highlights the utility of the Quantum Fisher Information, a measure of the sensitivity of a quantum state to small changes in parameters, framing the control problem as a reinforcement learning task and bypassing the need for explicit analytical modelling. The agent effectively learns control strategies through interaction with a simulated quantum system, proving particularly valuable when dealing with multi-parameter estimation.