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Memory-aware feedback enhances power in active information engines.

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Designing information engines for active environments requires new feedback strategies. Memory-preserving protocols exploit bath memory, outperforming conventional methods by maintaining performance in nonequilibrium systems.

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Area of Science:

  • Statistical mechanics
  • Non-equilibrium thermodynamics
  • Active matter physics

Background:

  • Active baths possess temporal correlations, unlike thermal baths, introducing memory effects.
  • Conventional feedback strategies optimized for memoryless systems may fail in active environments.
  • Stochastic thermodynamics needs extension to incorporate memory effects.

Purpose of the Study:

  • To analyze an information engine operating in an active bath with feedback-driven cycles.
  • To investigate the impact of bath memory on feedback control strategies.
  • To develop memory-preserving feedback protocols for enhanced engine performance.

Main Methods:

  • Extending stochastic thermodynamics to include memory effects.
  • Analyzing a feedback protocol with noisy position measurements and potential shifts.
  • Employing asymptotic analysis to study feedback gain effects.

Main Results:

  • Conventional feedback degrades performance by disrupting bath-particle memory.
  • Memory-preserving protocols exploit active bath temporal persistence.
  • Intermediate feedback gains are optimal in active media, outperforming full-shift resetting.

Conclusions:

  • Feedback strategies must account for bath memory in active systems.
  • Memory-preserving feedback offers a path to high-performance information engines.
  • Interplay of bath memory, noise, and feedback gain is critical for engine design.