Thermodynamic Cost and Benefit of Memory

Area of Science:

• Thermodynamics
• Information Theory
• Statistical Inference

Background:

• Information processing is fundamental to many scientific disciplines.
• Thermodynamic efficiency limits are crucial for energy-conscious technologies.
• Predictive inference plays a key role in understanding complex systems.

Purpose of the Study:

• To establish a connection between thermodynamic efficiency and predictive inference.
• To derive a generalized lower bound on dissipation for information engines.
• To develop a data representation method optimizing physical limits.

Main Methods:

• Derivation of a generalized lower bound on dissipation for partially observable information engines.
• Analysis of the impact of irrelevant information on efficiency.
• Optimization of a fundamental physical limit to information processing.

Main Results:

• A tight connection between thermodynamic efficiency and predictive inference is exposed.
• The retention of irrelevant information is shown to limit efficiency.
• Minimizing the lower bound on dissipation yields a compression method that maximally retains predictive information.

Conclusions:

• Predictive inference emerges as a strategy that minimizes energy dissipation.
• Optimized data representation enhances thermodynamic efficiency by prioritizing relevant information.
• This work provides a physical basis for predictive inference strategies.