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：我们基于自主量子热机建立了经典计算的物理模型。这些热机由少量相互作用的量子比特（qubits）组成，它们连接至多个处于不同温度的环境。此处利用流经热机的热流进行计算。流程开始于根据逻辑输入设定环境温度。随着热机演化并最终达到非平衡稳态，可通过辅助有限尺寸储热层的温度确定计算结果。我们将这种热机称为"热力学神经元"，它能够实现任意线性可分函数，并详细讨论了NOT门、三参数多数门和NOR门的案例。进一步地，我们证明热力学神经元网络能够执行任意所需函数。同时我们探讨了本模型与人工神经元（感知机）之间的紧密联系，论证了该模型可提供基于物理的神经网络替代性模拟实现方案，为热力学计算构建了更通用的平台。