The thermodynamic freedom of a thermodynamic computer

2026-08-28

AI Summary

研究使用Wasserstein速度限制评估了一个热力学计算机在执行机器学习分类任务时的热力学效率，展示了其在保持准确性和效率的同时，具有操作灵活性。

Abstract

Thermodynamic computers are stochastic physical devices designed to perform calculations at the thermal energy scale. Their operation is constrained by the equations of stochastic thermodynamics, among which are a set of bounds, known as speed limits, that relate a thermodynamic computer's run time to its computational progress and the heat it dissipates. Using the Wasserstein speed limit we assess the thermodynamic efficiency of a simulation model of a thermodynamic computer trained to perform a standard machine-learning classification task. On this task the thermodynamic computer is as capable as a simple multilayer perceptron. We show that different inference protocols allow the computer to operate within 40\% of the thermodynamic limit of efficiency without loss of accuracy, or to perform inference increasingly rapidly at fixed accuracy and thermodynamic efficiency. These results indicate that a thermodynamic computer designed for a particular task retains considerable freedom in its thermodynamic operation.

Problem

• thermodynamic computer
• stochastic thermodynamics
• speed limits
• thermodynamic efficiency
• inference protocols

Innovation

• Thermodynamic computer
• Wasserstein speed limit
• Thermodynamic efficiency
• Machine learning classification