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

Abstract

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.

5+22 pages, 6 figures

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• Many-body enhancement in a spin-chain quantum heat engine
• Optimal Thermometers with Spin Networks
• Dissipation-induced collective advantage of a quantum thermal machine
• Thermodynamic geometry of ideal quantum gases: a general framework and a geometric picture of BEC-enhanced heat engines
• Coherence-enhanced thermodynamic performance in a periodically-driven inelastic heat engine
• Fluctuations and stability of a fast driven Otto cycle
• Coherent control of thermoelectric currents and noise in quantum thermocouples
• Characterization and optimization of heat engines: Pareto-optimal fronts and universal features
• Optimising finite-time quantum information engines using Pareto bounds
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• Rapid optimal work extraction from a quantum-dot information engine
• Detecting Markovianity of Quantum Processes via Recurrent Neural Networks
• Optimizing power and efficiency of a single spin heat engine
• Making Non-Markovian master equations accessible with approximate environments