A unified, geometric framework for nonequilibrium protocol optimization

Understand

Controlling thermodynamic cycles to minimize the dissipated heat is a longstanding goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems.

• Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion.
• These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation.
• Furthermore, we show that the thermodynamic metric -- determined via a linear response approach -- can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries.