Driving rapidly while remaining in control: classical shortcuts from Hamiltonian to stochastic dynamics.

Area of Science:

• Thermodynamics
• Statistical Mechanics
• Non-equilibrium Physics

Background:

• Stochastic thermodynamics provides a framework for analyzing heat, work, and entropy in mesoscopic systems.
• This approach incorporates time into thermodynamic descriptions, enabling fine control over system evolution.

Purpose of the Study:

• To review strategies for finite-time state-to-state transformations in stochastic systems.
• To analyze control methods inspired by quantum control and reservoir engineering.
• To discuss optimality criteria and applications in mesoscopic engines and biological systems.

Main Methods:

• Review of finite-time control strategies for stochastic systems.
• Analysis of over and underdamped regimes with time-dependent control parameters.
• Exploration of inverse engineering, counterdiabatic driving, and fast-forward methods.
• Discussion of reservoir engineering for thermodynamic control.

Main Results:

• Finite-time control strategies enable precise state-to-state transformations in mesoscopic systems.
• Methods inspired by quantum control, such as inverse engineering and counterdiabatic driving, are effective.
• Optimal path determination is linked to cost functions and speed limits.
• Control through reservoir engineering offers additional pathways for manipulation.

Conclusions:

• The field of control in stochastic thermodynamics offers diverse applications, from mesoscopic heat engines to biological population control.
• Understanding optimal paths and speed limits is crucial for efficient thermodynamic control.
• Stochastic thermodynamics provides a powerful lens for manipulating mesoscopic systems in finite time.