Quantum imaginary time evolution steered by reinforcement learning

Abstract

Quantum imaginary time evolution – a common technique in theoretical studies to prepare ground states of quantum systems – comes with the uneasy requirement to implement non-unitary time evolution in the lab, and while recent solution has been proposed it carries leftover errors. The present work implements reinforcement learning to mitigate such errors in a physics-informed way, demonstrating the efficiency of AI-enhanced algorithms on a quantum computer.