Thermodynamic neurons enable interpretable rule-based machine learning through heat flow
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[Submitted on 24 Jun 2026]
Summary
This paper presents a novel machine learning architecture that uses thermodynamic neurons — autonomous quantum thermal machines — to implement logical operations through heat flow. The researchers construct a stochastic version of the Tsetlin machine, an interpretable rule-based learning system, using thermodynamic AND, NOT and OR gates with an autonomous coupling mechanism. Despite operating with noisy components, the classifier achieves accuracy statistically comparable to standard Tsetlin machines, with reliability arising from architectural mechanisms like thresholding and redundancy rather than exact logical operations. The work demonstrates that accurate and interpretable learning can emerge from autonomous stochastic dynamics.
Source
bskyThermodynamic neurons enable interpretable rule-based machine learning through heat flowarxiv.orgKey quotes
· 5 pulledWe turn these physical effects into computational resources for an autonomous, interpretable learning architecture.
We develop a classifier based on thermodynamic neurons, which are autonomous quantum thermal machines that implement logical operations through heat flow.
Despite its noisy components, the resulting classifier achieves classification accuracy that is statistically comparable to that of the standard Tsetlin machine.
Reliability arises from architectural mechanisms such as thresholding and redundancy, rather than exact logical operations.
Our results highlight that accurate and interpretable learning can emerge from autonomous stochastic dynamics, and establish thermodynamic computation as a viable framework for physical machine learning.
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