Battery Charging in Collision Models with Bayesian Risk Strategies

Autor(es)

Landi, Gabriel T. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Fis, BR-05314970 Sao Paulo - Brazil

^[2] Trinity Coll Dublin, Sch Phys, Coll Green 2, Dublin - Ireland

• Tipo de documento: Artigo Científico
• Fonte: Entropy; v. 23, n. 12 DEC 2021.
• Citações Web of Science: 0

Resumo

We constructed 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. (AU)

• Processo FAPESP: 19/14072-0 - Termodinâmica de precisão em dispositivos quânticos fora do equilíbrio
• Beneficiário: Gabriel Teixeira Landi
• Modalidade de apoio: Bolsas no Exterior - Pesquisa