AI 中文总结
研究组合优化问题，指出其计算要求高且可映射到伊辛模型。介绍光子伊辛机有望成为快速节能求解器，但当前实现方案受限。审视其现状，讨论挑战局限，确定实现大规模系统的进展，使其成为实用硬件平台。
AI 中文摘要
组合优化问题是物流、金融、工程和生命科学等诸多挑战的核心，但计算要求极高。许多此类问题可映射到伊辛模型，其中二元自旋通过耦合网络相互作用，解决方案对应低能、理想基态自旋构型。光子伊辛机有望借助光学的低延迟、高带宽和固有并行性成为快速且节能的优化问题启发式求解器。然而，当前光子实现方案在可扩展性、连通性、可重构性和求解时间方面仍受限，阻碍其在许多实际应用中的使用。在此视角下，我们审视光子伊辛机的现状，讨论现有平台的挑战与局限，并确定实现大规模系统所需的科技进展。这些发展可使光子伊辛机成为实用优化的有用硬件平台。
英文摘要
Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.
Comments29 pages, 5 figures
