Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

The full optimization of a quantum heat engine requires operating at high power, high efficiency, and high stability (i.e., low power fluctuations). However, these three objectives cannot be simultaneously optimized-as indicated by the so-called thermodynamic uncertainty relations-and a systematic approach to finding optimal balances between them including power fluctuations has, as yet, been elusive. Here we propose such a general framework to identify Pareto-optimal cycles for driven quantum heat engines that trade off power, efficiency, and fluctuations. We then employ reinforcement learning to identify the Pareto front of a quantum dot-based engine and find abrupt changes in the form of optimal cycles when switching between optimizing two and three objectives. We further derive analytical results in the fast- and slow-driving regimes that accurately describe different regions of the Pareto front.

Automated summary

The full optimization of a quantum heat engine requires trade-offs between power, efficiency, and fluctuations. A general framework is proposed to identify Pareto-optimal cycles that balance these objectives. Reinforcement learning is used to find the Pareto front of a quantum dot-based engine, revealing abrupt changes in optimal cycles when switching between optimizing two and three objectives. Analytical results accurately describe different regions of the Pareto front in fast- and slow-driving regimes.