Thermodynamic optimization of finite-time feedback protocols for Markov jump systems

Area of Science:

• Thermodynamics
• Information Theory
• Stochastic Processes

Background:

• Optimizing entropy production is crucial for efficient finite-time information processing.
• Feedback processes in classical discrete systems present unique thermodynamic challenges.

Purpose of the Study:

• To derive achievable bounds on entropy production for feedback processes controlled by Maxwell's demons.
• To identify the minimum entropy production required for information consumption and the optimal feedback protocol.

Main Methods:

• Utilizing optimal transport theory.
• Applying an achievable Fano's inequality.
• Optimizing Wasserstein distance over final distributions for fixed information consumption.

Main Results:

• Established achievable bounds on entropy production for Maxwell's demon controlled feedback processes.
• Determined the minimum entropy production necessary to consume a specific amount of information.
• Identified the optimal feedback protocol to achieve these minimum entropy production bounds.

Conclusions:

• The findings provide insights into the fundamental limits of information processing in stochastic systems.
• Results are expected to guide the design of efficient information processing protocols in discrete state systems.
• This work bridges thermodynamics and information theory for practical applications in feedback control.