In the steady regime, purely measurement-driven quantum engines extract no work because measurements become nondisturbing and inject no energy.
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An autonomous information machine operating in finite time exhibits a regime where erasure power P and efficiency η increase simultaneously, governed by the trade-off v(1-η)P/η ≤ D with D an information-geometric distance.
A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.
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No-Go Theorem for Quantum Heat Engines Powered Purely by Quantum Measurements in the Steady Regime
In the steady regime, purely measurement-driven quantum engines extract no work because measurements become nondisturbing and inject no energy.
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Finite-Time Thermodynamics of an Autonomous Information Machine
An autonomous information machine operating in finite time exhibits a regime where erasure power P and efficiency η increase simultaneously, governed by the trade-off v(1-η)P/η ≤ D with D an information-geometric distance.
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Quantifying information flow along a stochastic trajectory
A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.