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.

翻译：我们提出了一种基于自主量子热机的经典计算物理模型。这些机器由少数相互作用的量子比特组成，这些量子比特连接到多个不同温度的环境。计算通过利用流经机器的热流实现。计算过程首先根据逻辑输入设置各环境的温度。机器随之演化，最终达到非平衡稳态，此时可通过一个辅助有限尺寸热库的温度确定计算输出。我们将此类机器称为“热力学神经元”，它能够实现任何线性可分函数，并具体讨论了NOT门、3-多数表决门和NOR门的实现案例。进一步地，我们证明由热力学神经元构成的网络可以执行任意期望函数。我们探讨了该模型与人工神经元（感知机）的紧密联系，论证了该模型为神经网络提供了一种基于物理原理的模拟实现方案，更广义而言，为热力学计算提供了一个平台。