A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing

TL;DR - This paper proposes a thermodynamic computing stack that uses noise-driven analog hardware and tunable energy potentials to train and sample probabilistic machine-learning models. It could reduce the energy use and latency of ML workloads.

• Models stochastic computation through Langevin dynamics and hardware-native energy-based models.
• Uses probabilistic graphical models to construct and train established ML model classes.
• Analyzes runtime and energy consumption theoretically and numerically.
• Presents thermal-noise-driven superconducting circuits as a preliminary hardware realization.