Speaker
Description
Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy, as exemplified by the famous Maxwell’s demon thought experiment that led to the notorious and important Landauer’s bound. In this talk I will start by investigating the impact of genuine quantum features on Landauer’s erasure in the slow driving regime, demonstrating that quantum coherence generated in the energy eigenbasis of a system undergoing a finite-time information erasure protocol yields rare events with extreme dissipation. The second part of this talk will be devoted to a different aspect of the same problem, namely the one of deriving optimal feedback control strategies. This highly challenging task gets even richer in the quantum regime, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In a recent work we employed a reinforcement learning approach to automate and capture the role of a quantum Maxwell’s demon: the agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency.
