Lipka-Bartosik Patryk, Perarnau-Llobet Martí, Brunner Nicolas
Department of Applied Physics, University of Geneva, 1211 Geneva, Switzerland.
Sci Adv. 2024 Sep 6;10(36):eadm8792. doi: 10.1126/sciadv.adm8792. Epub 2024 Sep 4.
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 nonequilibrium 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 analog implementation of neural networks, and more generally a platform for thermodynamic computing.
我们基于自主量子热机开发了一种用于经典计算的物理模型。这些机器由少数相互作用的量子比特（qubit）组成，这些量子比特连接到几个处于不同温度的环境。这里利用流经机器的热流来进行计算。该过程首先根据逻辑输入设置环境温度。机器演化，最终达到非平衡稳态，从该稳态可以通过辅助有限尺寸储能器的温度确定计算输出。这样一种机器，我们称之为“热力学神经元”，可以实现任何线性可分函数，并且我们明确讨论了非门、3取多数门和或非门的情况。反过来，我们表明热力学神经元网络可以执行任何所需的功能。我们讨论了我们的模型与人工神经元（感知器）之间的紧密联系，并认为我们的模型提供了一种基于物理的神经网络替代模拟实现，更一般地说，是一个用于热力学计算的平台。
