发表机构
Extropic Corporation（Extropic公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一套工具，将通用随机程序映射到热力学硬件，通过thermalizers框架编译随机程序并优化误差，在市场模拟器等多个应用中验证了其有效性。
AI 中文摘要
我们提出了一套工具，用于将通用随机程序映射到专为高能效随机采样设计的热力学硬件。给定以随机通道的有向因子图（DFG）形式表示的目标随机程序，或等价地以参数化随机电路（PSC）形式表示的目标随机程序，我们首先引入一种方法，将DFG中的每个因子近似编译为硬件原生的基于能量的模型（EBM）。随后，我们分析编译后的DFG的误差如何从各因子的误差中累积，并引入两种训练优化方法：上下文匹配和轨迹级REINFORCE后训练，可减少单独训练每个因子后残留的误差。thermalizers框架接受以torx库表示的随机程序，并用thrml库实现和采样的热力学内核替换其因子。我们在多个示例应用中展示了该框架，包括仅从记录的市场历史中学习一组金融时间序列的联合日间动态的市场模拟器、数学生态学中的概率模型、硬件无法原生表达的EBM的吉布斯采样，以及高斯随机电路上的顺序贝叶斯设计循环。
英文摘要
We present a set of tools for mapping general stochastic programs to thermodynamic hardware designed for energy-efficient stochastic sampling. Given a target stochastic program expressed as a Directed Factor Graph (DFG) of stochastic channels, or equivalently as a Parametrized Stochastic Circuit (PSC), we first introduce a method to approximately compile each factor in the DFG to an Energy-Based Model (EBM) that is native to the hardware. We then analyze how the error of the compiled DFG accumulates from the per-factor errors, and introduce two training refinements, context matching and trajectory-level REINFORCE post-training, which can reduce the residual error left by training each factor in isolation. The \texttt{thermalizers} framework takes a stochastic program expressed in the \texttt{torx} library and replaces its factors with thermodynamic kernels implemented and sampled using the \texttt{thrml} library. We demonstrate it on several example applications, including a market simulator that learns the joint day-to-day dynamics of a panel of financial time series from recorded market history alone, a probabilistic model from mathematical ecology, Gibbs sampling of an EBM the hardware cannot natively express, and a sequential Bayesian design loop over a Gaussian stochastic circuit.
