Thermodynamic Computing via Autonomous Quantum Thermal Machines

TL;DR

This paper introduces a physics-based model for classical computation using autonomous quantum thermal machines, where heat flows enable logic operations and neural network functionalities in a thermodynamic setting.

Contribution

The authors propose a novel thermodynamic neuron model based on quantum thermal machines, enabling linearly-separable functions and neural network architectures through heat flow manipulation.

Findings

Thermodynamic neurons can implement NOT, 3-MAJORITY, and NOR gates.

Networks of thermodynamic neurons can perform arbitrary functions.

The model offers a physics-based analogue platform for neural networks.

Abstract

We develop a physics-based model for classical computation based on autonomous quantum thermal machines. These machines consist of few interacting quantum bits (qubits) connected to several environments at different temperatures. Heat flows through the machine are here exploited for computing. The process starts by setting the temperatures of the environments according to the logical input. The machine evolves, eventually reaching a non-equilibrium steady state, from which the output of the computation can be determined via the temperature of an auxilliary finite-size reservoir. Such a machine, which we term a ``thermodynamic neuron'', can implement any linearly-separable function, and we discuss explicitly the cases of NOT, 3-MAJORITY and NOR gates. In turn, we show that a network of thermodynamic neurons can perform any desired function. We discuss the close connection between our model and artificial neurons (perceptrons), and argue that our model provides an alternative physics-based analogue implementation of neural networks, and more generally a platform for thermodynamic computing.