Probabilistic algorithms enhance Ising machines for optimization and AI
Probabilistic Algorithms for Ising Machines from Optimization to Generative AI
Machine LearningEmerging Technologies
Summary
Ising machines are special computers designed to solve really hard problems by trying many possible answers quickly. The authors review different smart methods that help these machines work better across many types of hardware. They also show how combining these methods with AI ideas can speed up solving problems and improve large language models. This paper looks at how designing both algorithms and hardware together can make these machines more useful in the future.
What this means in practice
- •For optimization engineers: Use principled probabilistic methods tailored for Ising hardware to improve solving large-scale complex optimization problems.
- •For natural language processing teams: Incorporate probabilistic techniques from Ising machines to enhance inference speed and accuracy in large language models.
A survey. It maps existing work.
Authors
Corentin Delacour, Xiuqi Zhang, Abdelrahman S. Abdelrahman, Saleh Bunaiyan, Kyle Lee, Shuvro Chowdhury, Kerem Y. Camsari
Abstract
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.