发表机构
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了面向Ising机器的概率算法，涵盖从模拟退火到PAOA等方法，并探讨了生成式AI与Ising机器的相互促进，提出了加速下一代硬件应用的协同设计框架。
AI 中文摘要
Ising机器已成为解决棘手优化和采样问题的有前景的硬件加速器，然而其实际影响日益取决于算法与硬件的协同设计，其中算法需求塑造新架构，新硬件能力激发全新算法。在本综述中，我们调查了为在不同Ising平台上可移植性而设计的概率算法，倡导一种自上而下的视角，优先考虑具有可证明保证的原则性方法。我们涵盖了诸如模拟退火和并行回火等基础方法，包括原生编码硬约束的二维扩展，并考察了从簇平均场方法到变分采样器等扩大可解问题规模的方法。我们重点介绍了概率近似优化算法（PAOA），这是QAOA的经典类比，直接源于概率硬件开发，并探讨了生成式人工智能与Ising机器如何相互促进：学习模型提出全局移动以加速优化，而概率技术则改进大型语言模型中的推理。正如量子计算见证了算法与硬件的共同演进，概率和Ising计算正处于类似的拐点。我们概述了一个协同设计框架，以加速下一代Ising机器的能力和采用。
英文摘要
Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasingly hinges on the co-design of algorithms and hardware, where algorithmic demands shape new architectures and new hardware capabilities inspire entirely new algorithms. In this Review, we survey probabilistic algorithms designed for portability across diverse Ising platforms, advocating a top-down perspective that prioritizes principled methods with provable guarantees. We cover foundational methods such as simulated annealing and parallel tempering, including two-dimensional extensions that natively encode hard constraints, and examine approaches that expand the scale of solvable problems from cluster mean-field methods to variational samplers. We highlight the Probabilistic Approximate Optimization Algorithm (PAOA), a classical analog of QAOA that emerged directly from probabilistic hardware development, and explore how generative AI and Ising machines might reinforce each other: learned models propose global moves to accelerate optimization, while probabilistic techniques improve inference in large language models. Much as quantum computing has seen algorithms co-evolve with hardware, probabilistic and Ising computing stand at a similar inflection point. We outline a co-design framework for accelerating the capabilities and adoption of next-generation Ising machines.
DOI：10.1038/s44287-026-00344-0
