Topological Engine Monitor: Persistent Homology-Based Fault Detection in Finite-Time Quantum Engines

Abstract:The reliable operation of finite-time quantum heat engines is fundamentally limited by control imperfections that induce nonadiabatic phase accumulation and quantum friction, degrading the stability of the thermodynamic cycle. Traditional monitoring relies on energetic observables such as instantaneous cycle work; however, under finite-time driving, these quantities exhibit strong fluctuations, obscuring reliable single-shot fault detection without extensive statistical averaging. Here, we apply a topological data analysis (TDA)-based approach to establish a non-invasive, purely geometric framework for diagnosing control failures in finite-time quantum Otto engines. We construct time-delay embeddings from weak measurements and map the dynamics into persistent homology diagrams. We define a scalar quality index based on Wasserstein and Bottleneck distances that tracks control degradation and anticipates cyclic failure. By encoding topology via persistence images and silhouettes, we achieve highly robust classification of degraded operation across diverse noise profiles. We benchmark the TDA-based approach (topological engine monitor, TEM) against a standard multi-feature statistical baseline (spectral-statistical monitor, SSM) across progressively realistic noise settings, from global timing jitter to correlated adiabatic noise and coherence injection. We find that as noise becomes more localized and realistic, the conventional SSM approach degrades while the TEM remains robust. Finally, a pixel-wise Pearson correlation analysis reveals that the method captures microscopic signatures of quantum friction. Our results demonstrate the potential of topology-based diagnostics for non-ideal quantum thermodynamic devices.

arXiv:2604.11289 [quant-ph]

(or arXiv:2604.11289v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2604.11289 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1088/2058-9565/ae98eb Focus to learn more DOI(s) linking to related resources

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