Battery Charging in Collision Models with Bayesian Risk Strategies
1Instituto de Física, Universidade de São Paulo, São Paulo 05314-970, Brazil.
Entropy (Basel, Switzerland)
|December 24, 2021
Summary
This study demonstrates an autonomous quantum system that sorts quantum batteries based on their extractable work (ergotropy). A Maxwell demon-like process enhances ergotropy by resetting information using a cold bath.
Area of Science:
- Quantum thermodynamics
- Quantum information theory
Background:
- Quantum batteries store energy in quantum states.
- Ergotropy quantifies the maximum extractable work from quantum systems.
- Autonomous quantum systems require efficient energy management.
Purpose of the Study:
- To develop an autonomous quantum system for enhancing ergotropy.
- To implement a Maxwell demon-like process for quantum batteries.
- To classify and process quantum ancillas based on their energy content.
Main Methods:
- Construction of a collision model for quantum ancilla interaction.
- Utilizing Bayesian decision rules for ergotropy classification.
- Employing a cold heat bath for autonomous information reset.
Main Results:
- Successful classification of ancillas into high and low ergotropy groups.
- Demonstration of a process that redirects low-ergotropy ancillas for further work extraction.
- Establishment of an autonomous cycle with a well-defined limit.
Conclusions:
- The developed collision model effectively functions as a Maxwell demon for quantum batteries.
- Autonomous ergotropy enhancement is achievable through information reset mechanisms.
- This approach offers a pathway for optimizing energy extraction in quantum systems.
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