Robust Quantum Control via Optimising a Stochastic Target Functional

Jayant Misra


Supervised by Frank C Langbein; Moderated by Matthew J W Morgan

An important part to turn quantum physics into technologies is the robustness of the quantum process, e.g. for quantum computing, sensing or simulation. Quantum control provides the means to steer quantum processes, but often it is focused on high fidelity and not also robustness of the process. We developed a stochastic measure to judge the fidelity and robustness of a quantum process. The aim of this project is to investigate methods that optimise this measure directly to find a robust, high-fidelity control. There is a range of optimisation methods (from gradient-based to gradient-free methods; stochastic optimisation, evolutionary or genetic methods, or even machine learning/reinforcement learning approaches) from which some can be selected for detailed exploration of their ability to optimise the robustness measure. Results will feed into our research work on robust quantum control. You need sufficiently powerful hardware (mostly CPU and memory, but some optimisation approaches may require a GPU); the Linux lab machines or resources from SCW are available. Details must be discussed with me and the approach fixed during selection. Potentially multiple projects can explore different approaches.

Initial Plan (06/02/2023) [Zip Archive]

Final Report (19/05/2023) [Zip Archive]

Publication Form