Optimising finite-time quantum information engines using Pareto bounds

TL;DR

This paper analyzes the fundamental trade-offs in finite-time quantum information engines, using Pareto optimization to guide design principles for improved performance in quantum thermodynamic systems.

Contribution

It introduces a Pareto optimization framework to study trade-offs in finite-time quantum information engines, providing new insights for their design and implementation.

Findings

Identifies optimal trade-offs between power, efficiency, and information-to-work conversion.

Provides Pareto frontiers for quantum engine performance metrics.

Offers design principles for experimental quantum information engines.

Abstract

Information engines harness measurement and feedback to convert energy into useful work. In this study, we investigate the fundamental trade-offs between ergotropic output power, thermodynamic efficiency and information-to-work conversion efficiency in such engines, explicitly accounting for the finite time required for measurement. As a model engine, we consider a two-level quantum system from which work is extracted via a temporarily coupled quantum harmonic oscillator that serves as the measurement device. This quantum device is subsequently read out by a classical apparatus. We compute trade-offs for the performance of the information engine using Pareto optimisation, which has recently been successfully used to optimise performance in engineering and biological physics. Our results offer design principles for future experimental implementations of information engines, such as in nano-mechanical systems and circuit QED platforms.