Learning to control quantum systems robustly

Langbein, Frank C. ORCID: https://orcid.org/0000-0002-3379-0323 2021. Learning to control quantum systems robustly. Presented at: AI3SD Autumn Seminar 2021 - Quantum Machine Learning, Virtual, 10 November 2021.

Preview

PDF - Presentation Available under License Creative Commons Attribution Non-commercial Share Alike. Download (6MB) | Preview

Abstract

Quantum control provides methods to steer the dynamics of quantum systems. The robustness of such controls, in addition to high fidelity, is important for practical applications due to the presence of uncertainties arising from limited knowledge about system and control Hamiltonians, initial state preparation errors, and interactions with the environment leading to decoherence. We introduce a novel robustness measure based on the Wasserstein distance, and discuss structured singular value analysis and log-sensitivity approaches from classical robust control. This is employed to analyse the robustness of controllers found by reinforcement learning and gradient-based optimisation algorithms. Some, not all, high-fidelity controllers are also robust and controllers found by reinforcement learning appear less affected by noise than those found by gradient-based optimisation. We briefly discuss applications in information transfer in spin networks and magnetic resonance spectroscopy.

Item Type:	Conference or Workshop Item - unpublished
Date Type:	Published Online
Status:	Unpublished
Schools:	Schools > Computer Science & Informatics
Last Modified:	16 Jul 2026 10:50
URI:	https://orca.cardiff.ac.uk/id/eprint/149266

Actions (repository staff only)

Edit Item