Carnot Meets Quantum Information: Thermal Machine Driven by Probabilistic Non-orthogonal State Discrimination
Tan-Ji Zhou, Yun-Qian Lin, Yu-Han Ma, C. P. Sun
Abstract
While the impossibility of perfectly identifying non-orthogonal states is a cornerstone of quantum information science, their probabilistic discrimination is nonetheless permissible. Here, we propose a two-reservoir quantum machine driven by this mechanism to map its functional boundaries across the parameter space of the state overlap μ and the Carnot efficiency ηC. Within this ηC-μ plane, the machine exhibits phase-transition-like functional switching among a pure heat-engine phase, a mixed phase, and a dissipative phase. We identify critical thresholds governing these transitions: strong thermal driving (ηC 0.5) unconditionally guarantees positive work extraction, whereas weak driving (ηC 0.13) induces an anomalous reentrant transition, where increasing μ unexpectedly restores engine functionality after a purely dissipative regime. Our results explicitly demonstrate how quantum mechanics and thermodynamics jointly constrain information-to-energy conversion.
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