近日，加拿大女王大学Bhavin J. Shastri团队报道了受Hopfield启发的可编程200 GOPS光子伊辛机。这一研究成果于2025年12月17日发表在《自然》期刊上。

伊辛机为求解NP难问题提供了极具前景的路径，但兼具可扩展性、可重构性、高速性与稳定性的物理实现方案仍待突破。以D-Wave低温硬件为代表的量子退火器虽瞄准组合优化任务，但其在稠密图问题上所需量子比特数量随问题规模呈二次方增长，限制了可扩展性。

研究组提出一种基于光电振荡器的可编程、稳定、室温运行的伊辛机，其自旋表征呈现线性扩展特性。受霍普菲尔德网络启发，该架构可求解包含最多256个自旋（65,536个耦合）的全连接问题；若问题具有稀疏性，则可扩展至超过41,000个自旋（205,000+个耦合）。该系统采用循环时间编码环结构，集成了级联薄膜铌酸锂调制器、半导体光放大器与数字信号处理引擎，在自旋耦合和非线性运算方面展现出超过200吉次操作/秒的潜力。凭借固有的高运算速度，该平台实现了当前光电振荡器基光子伊辛机中最大规模的自旋配置。

实验证明，在各类光子伊辛机中，该系统对任意拓扑结构的最大割问题（2,000与20,000个自旋）取得了最优求解质量，并获得了数字划分与晶格蛋白质折叠问题的基态解——这两类基准问题此前尚未被光子计算系统攻克。系统利用高波特率产生的本征噪声逃离局部极小值并加速收敛。最后，研究组展示了将传统光通信领域的数字信号处理技术嵌入光计算中，能够显著提升收敛速度与求解质量，为可扩展超快计算在优化、神经形态处理与模拟人工智能领域开辟了新前沿。

附：英文原文

Title: Programmable 200 GOPS Hopfield-inspired photonic Ising machine

Author: Al-Kayed, Nayem, St-Arnault, Charles, Morison, Hugh, Aadhi, A., Huang, Chaoran, Tait, Alexander N., Plant, David V., Shastri, Bhavin J.

Issue&Volume: 2025-12-17

Abstract: Ising machines offer a compelling approach to addressing NP-hard problems1, but physical realizations that are simultaneously scalable, reconfigurable, fast and stable remain elusive. Quantum annealers, such as D-Wave’s cryogenic hardware, target combinatorial optimization tasks, but quadratic scaling of qubit requirements with problem size limits their scalability on dense graphs2. Here we introduce a programmable, stable, room-temperature optoelectronic oscillator (OEO)-based Ising machine with linear scaling in spin representation. Inspired by Hopfield networks3, our architecture solves fully connected problems with up to 256 spins (65,536 couplings) and >41,000 spins (205,000+ couplings) if sparse. Our system makes use of cascaded thin-film lithium niobate (TFLN) modulators, a semiconductor optical amplifier (SOA) and a digital signal processing (DSP) engine in a recurrent time-encoded loop, demonstrating potential >200giga operations per second (GOPS) for spin coupling and nonlinearity. This platform achieves the largest spin configuration in an OEO-based photonic Ising machine, enabled by high intrinsic speed. We experimentally demonstrate best-in-class solution quality for max-cut problems of arbitrary graph topologies (2,000 and 20,000 spins) among photonic Ising machines and obtain ground-state solutions for number partitioning4 and lattice protein folding5—benchmarks previously unaddressed by photonic systems. Our system uses inherent noise from high baud rates to escape local minima and accelerate convergence. Finally, we show that embedding DSP—traditionally used in optical communications—within optical computation enhances convergence and solution quality, opening new frontiers in scalable, ultrafast computing for optimization, neuromorphic processing and analogue artificial intelligence.

DOI: 10.1038/s41586-025-09838-7

Source: https://www.nature.com/articles/s41586-025-09838-7