用热涨落实现非线性计算，不依赖热平衡状态。
Nonlinear thermodynamic computing out of equilibrium
- 热涨落系统通过四次势阱约束，实现输入到输出的非线性响应。
- 遗传算法可调节参数，在指定时间完成非线性计算，无论是否达热平衡。
- 适用于非平衡热计算场景，为新型计算架构提供可能。
我们提出一种热力学计算机设计，可在热平衡或非平衡状态下执行任意非线性计算。由热浴中涨落自由度构成的简单热力学电路，受四次势阱约束，其活性表现为输入的非线性函数，可视为热力学神经元。这些元件可组合成热力学神经网络，作为通用函数逼近器，其运行依赖于热涨落。我们模拟了热力学神经网络的数字模型，证明其参数可通过遗传算法调整，使系统在特定观测时刻完成非线性计算，且无需达到热平衡。该研究将热力学计算拓展至非平衡域，实现了类经典神经网络的全非线性运算。
原文摘要 · Abstract (English)
We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.
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