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

2026-07-17

AI Summary

This work addresses the growing challenges of energy consumption and latency in machine learning by proposing a differentiable thermodynamic computing architecture tailored for continuous variables, which deeply integrates energy-based models with physical hardware. Leveraging Langevin dynamics, the approach enables hardware-native probabilistic inference and learning within stochastic superconducting circuits endowed with tunable energy potentials, harnessing thermal noise-driven physical processes to efficiently train probabilistic graphical models. Theoretical analysis and numerical experiments demonstrate that this paradigm substantially reduces both energy expenditure and computational latency. Preliminary hardware validation is achieved through a superconducting circuit prototype, offering a promising new pathway toward low-power, high-efficiency probabilistic machine learning.

Abstract

To address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in physical hardware. In this work, we focus on energy-based thermodynamic computing where the stochastic process is well described by Langevin dynamics with tunable energy potentials. The implementation of such potentials in physical hardware enables us to generate and sample from basic parameterized energy-based models. We demonstrate how to construct and train popular classes of machine learning models based on these hardware-native energy-based models, using the framework of probabilistic graphical models. We analyze the runtime and energy consumption of different models in this thermodynamic paradigm based on theoretical considerations and numerical studies. As a preliminary experimental realization of such hardware, we present our stochastic analog superconducting circuits driven by thermal noise. Together, these results outline a path toward energy-efficient thermodynamic hardware for probabilistic machine learning.

Problem

• thermodynamic computing
• energy efficiency
• machine learning
• stochastic analog hardware
• energy-based models

Innovation

• thermodynamic computing
• energy-based models
• Langevin dynamics
• stochastic analog hardware
• probabilistic machine learning