Battery charging in collision models with Bayesian risk strategies

Abstract

We construct a collision model where measurements in the system, together with a Bayesian decision rule, are used to classify the incoming ancillas as having either high or low ergotropy (maximum extractable work). The former are allowed to leave, while the latter are redirected for further processing, aimed at increasing their ergotropy further. The ancillas play the role of a quantum battery, and the collision model therefore implements a Maxwell demon. To make the process autonomous, and with a well defined limit cycle, the information collected by the demon is reset after each collision by means of a cold heat bath.

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Cited by in corpus (9)

• Quantum collision models: open system dynamics from repeated interactions
• Superconducting transmon qubit-resonator quantum battery
• Quantum advantage in charging cavity and spin batteries by repeated interactions
• Charging a quantum battery in a non-Markovian environment: a collisional model approach
• Work extraction from unknown quantum sources
• Lossy Micromaser Battery: Almost Pure States in the Jaynes-Cummings Regime
• Efficiency fluctuations in a quantum battery charged by a repeated interaction process
• Extracting work from random collisions: A model of a quantum heat engine
• Quantum Work Capacitances: ultimate limits for energy extraction on noisy quantum batteries