Weak Gaussian noise in control fields makes dissipation grow linearly with steps in quantum equilibration, yielding a finite optimal step count and minimal dissipated work derived from quantum thermodynamic length.
Seifert, Stochastic thermodynamics, fluctuation the- orems and molecular machines, Rep
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Multi-time correlations of state observations are combined via a reconstruction operation into a hierarchy of successively tighter lower bounds on entropy production rate that converge to the true value with dense sampling.
A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.
Data-driven framework using short-time TUR inference and deep neural networks reconstructs high-dimensional dissipative force fields and localizes fluctuating entropy production in space and time.
Temperature requires continuous photon energy input averaging 2.701 times the characteristic energy E_c to offset radiative losses and sustain the Planck distribution.
citing papers explorer
-
Finite steps optimise dissipation in stochastically controlled quantum systems
Weak Gaussian noise in control fields makes dissipation grow linearly with steps in quantum equilibration, yielding a finite optimal step count and minimal dissipated work derived from quantum thermodynamic length.
-
Hierarchical Reconstruction of Time-arrow from Multi-time Correlations
Multi-time correlations of state observations are combined via a reconstruction operation into a hierarchy of successively tighter lower bounds on entropy production rate that converge to the true value with dense sampling.
-
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.
-
Localizing entropy production along non-equilibrium trajectories
Data-driven framework using short-time TUR inference and deep neural networks reconstructs high-dimensional dissipative force fields and localizes fluctuating entropy production in space and time.
-
Temperature as a Dynamically Maintained Steady State: Photonic Mechanisms, Maintenance Cost, and the Limits of the Infinite-Reservoir Idealization
Temperature requires continuous photon energy input averaging 2.701 times the characteristic energy E_c to offset radiative losses and sustain the Planck distribution.