Capturing dynamics and thermodynamics of a three-level quantum heat engine via programmable quantum circuits

TL;DR

This paper models a three-level quantum heat engine using programmable quantum circuits, demonstrating its dynamic and thermodynamic behavior, and optimizing its cycle with reinforcement learning to reduce experimental costs.

Contribution

It introduces a novel approach combining Kraus representation, Sz.-Nagy dilation, and reinforcement learning for simulating and optimizing quantum heat engines on quantum circuits.

Findings

Successful modeling of quantum heat engine dynamics and thermodynamics.

Validation of quantum circuit simulations against theoretical results.

Potential to significantly reduce experimental costs in quantum heat engine development.

Abstract

This research employs the Kraus representation and Sz.-Nagy dilation theorem to model a three-level quantum heat on quantum circuits, investigating its dynamic evolution and thermodynamic performance. The feasibility of the dynamic model is validated by tracking the changes of population. On the basis of reinforcement learning algorithm, the optimal cycle of the quantum heat engine for maximal average power is proposed and verified by the thermodynamic model. The stability of quantum circuit simulations is scrutinized through a comparative analysis of theoretical and simulated results, predicated on an orthogonal test. These results affirm the practicality of simulating quantum heat engines on quantum circuits, offering potential for substantially curtailing the experimental expenses associated with the construction of such engines.