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Time-Resolved Stochastic Dynamics of Quantum Thermal Machines.

Abhaya S Hegde1, Patrick P Potts2, Gabriel T Landi1

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This summary is machine-generated.

We developed a framework to analyze quantum thermal machines by classifying their dynamics into enginelike, coolinglike, or idle cycles. This helps determine useful cycle fractions and operational consistency, crucial for quantum dot experiments.

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Area of Science:

  • Quantum thermodynamics
  • Mesoscopic physics
  • Statistical mechanics

Background:

  • Quantum thermal machines exhibit continuous heat flow at steady state.
  • Discrete quantum jumps represent finite heat exchange with the environment.
  • Understanding cycle dynamics is key to characterizing machine performance.

Purpose of the Study:

  • To resolve quantum thermal machine dynamics into discrete cycles.
  • To analyze cycle statistics and durations for thermodynamic tasks.
  • To introduce intermittency as a measure of operational consistency.

Main Methods:

  • Framework for classifying dynamics into enginelike, coolinglike, and idle cycles.
  • Statistical analysis of individual cycle types and their durations.
  • Assessment of intermittency through idle cycle frequency and distribution.

Main Results:

  • Quantification of the fraction of cycles useful for thermodynamic tasks.
  • Determination of average waiting times between specific cycle types.
  • Characterization of operational consistency using intermittency.

Conclusions:

  • The framework provides a novel approach to characterizing quantum thermal machines.
  • Analysis of cycle statistics and intermittency offers deeper insights into machine performance.
  • Significant relevance for experiments on mesoscopic transport in quantum dots.