Spin systems are a powerful tool for modeling a wide range of physical systems. In this paper, we propose a novel framework for modeling spin systems using differentiable programming. Our approach enables us to efficiently simulate spin systems, making it possible to model complex systems at scale. Specifically, we demonstrate the effectiveness of our technique by applying it to three different spin systems: the Ising model, the Potts model, and the Cellular Potts model. Our simulations show that our framework offers significant speedup compared to traditional simulation methods, thanks to its ability to execute code efficiently across different hardware architectures, including Graphical Processing Units and Tensor Processing Units.

翻译：自旋系统是建模广泛物理系统的强大工具。本文提出了一种利用可微编程技术建模自旋系统的新框架。该方法能够高效模拟自旋系统，从而支持大规模复杂系统的建模。具体而言，我们通过将其应用于伊辛模型（Ising model）、波茨模型（Potts model）和细胞波茨模型（Cellular Potts model）三种不同的自旋系统，验证了该技术的有效性。模拟结果表明，由于该框架能够在不同硬件架构（包括图形处理器GPU和张量处理器TPU）上高效执行代码，其计算速度相较传统模拟方法显著提升。