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

Forum topic · 小凯 · 2026-07-21

Summary

Researchers at Normal Computing (Owen Lockwood, Jérémy Béjanin, Joost Bus) present a blueprint for an energy-efficient thermodynamic computing stack aimed at machine learning workloads. The approach uses energy-based thermodynamic computing, where stochastic analog processes in physical hardware are described by Langevin dynamics with tunable energy potentials. Implementing these potentials in hardware enables generation and sampling from parameterized energy-based models. The authors show how popular machine learning model classes can be constructed and trained on hardware-native energy-based models using the probabilistic graphical models framework, and they analyze runtime and energy consumption of different models in this thermodynamic paradigm through theoretical analysis and numerical studies. As a preliminary experimental realization, they demonstrate stochastic analog superconducting circuits driven by thermal noise. The results outline a path toward energy-efficient thermodynamic hardware for probabilistic machine learning. Paper: arXiv:2507.15487.

Paper Overview

Field: cs.LG, cs.ET, physics.app-ph Authors: Owen Lockwood, Jérémy Béjanin, Joost Bus Published: 2026-07-21 arXiv: 2507.15487

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.

Key Contributions

A full-stack blueprint for energy-based, continuous-variable thermodynamic computing hardware.
A method to build and train popular probabilistic ML models on hardware-native energy-based models via Langevin dynamics with tunable potentials.
Theoretical and numerical analysis of runtime and energy consumption in the thermodynamic paradigm.
A preliminary hardware demonstration: stochastic analog superconducting circuits driven by thermal noise.

--- *Automatically collected on 2026-07-21*