Coherence-Preserving Fluctuation Diagnostics for an Engineered Population-Inverted Qubit Otto Engine

TL;DR

This paper introduces a coherence-preserving fluctuation diagnostic for a quantum Otto engine, enabling analysis of work, heat, and efficiency fluctuations without projective measurements, revealing regimes of enhanced power and stability.

Contribution

It develops a measurement-backaction-free method using a dynamic Bayesian network to analyze quantum engine fluctuations, highlighting coherence effects and operational regimes.

Findings

Population inversion enhances work and power output.

Structured operating landscape with high-power and high-efficiency sectors.

DBN predictions diverge from traditional methods in coherence-rich regimes.

Abstract

Finite-time quantum thermal machines require diagnostics beyond average work and efficiency, because microscopic engines operate in regimes where fluctuations, incomplete thermalization, and coherence are equally important. Here we develop a measurement-backaction-free (coherence-preserving) fluctuation diagnostic for an engineered qubit Otto engine coupled to an actively maintained population-inverted hot channel. The engine is analyzed using a dynamic Bayesian network (DBN) reconstruction of the unmeasured coherent cycle, yielding work, heat, power, and normalized efficiency-proxy fluctuations without imposing the projective dephasing inherent in two-point energy measurements. The inverted channel is treated as an active reduced-model resource; accordingly, all reported power and efficiency enhancements represent gross working-medium advantages, not net device efficiencies. In the full-thermalization limit, population inversion enhances extracted work and output power while opening a stability sector with markedly reduced relative power fluctuations. When finite-duration isochores are implemented, this gross enhancement reorganizes into a structured operating landscape with distinct high-power, high-efficiency, and low-relative-noise sectors, whose boundaries are governed by the competing timescales of nonadiabatic driving and thermalization rates. A direct comparison reveals that DBN and conventional two-point measurement predictions diverge precisely in coherence-rich regimes, identifying where a backaction-free reconstruction is essential. A coherence-sensitive analysis further shows that the positive-temperature reference operates optimally in an almost decohered region, whereas the inverted high-efficiency branch remains aligned with the dominant post-hot-bath coherence ridge. These results provide a reduced-model benchmarking framework for engineered qubit thermal machines.