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This study derives a novel lower bound for steady-state current noise in complex systems using network analysis and graph theory. The findings offer new insights into system fluctuations and computational irreversibility.

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Area of Science:

  • Statistical Mechanics
  • Network Theory
  • Non-equilibrium Physics

Background:

  • Master equation describes system dynamics.
  • Network analysis and graph theory are powerful tools for studying complex systems.
  • Understanding steady-state current noise is crucial in non-equilibrium systems.

Purpose of the Study:

  • To estimate the lower bound of steady-state current noise using network analysis.
  • To derive a noise lower bound applicable to systems driven to a non-equilibrium steady state.
  • To apply this bound to analyze computational time fluctuations and logical irreversibility.

Main Methods:

  • Network analysis of systems described by the master equation.
  • Utilizing level 2.5 large deviation functions.
  • Employing graph theory and mesh currents for noise bound derivation.

Main Results:

  • A novel lower bound for steady-state current noise was derived.
  • The bound accounts for sojourn time fluctuations across all states.
  • Applied to a Brownian computation with reset, the bound captures logical irreversibility, unlike entropy-based bounds.

Conclusions:

  • The derived noise lower bound provides a new metric for system fluctuations.
  • This approach offers a way to quantify logical irreversibility in computational processes.
  • The study advances the understanding of noise in non-equilibrium systems.