Fully Parallel Multi-Agent Photonic Optimizer

This concerns the manuscript "Fully Parallel Multi-Agent Photonic Optimizer"

Abstract: Optimization problems are central to many important cross-disciplinary applications. In their conventional implementations, the sequential nature of operations imposes strict limitations on the computational efficiency. Here, we discuss how analog optical computing can overcome this fundamental bottleneck. We propose a photonic optimizer unit, together with supporting algorithms that uses in-memory computation within a nature-inspired, multi-agent cooperativeframework. The system performs a sequence of reconfigurable parallel matrix–vector operations, enabled by the highbandwidth and multiplexing capabilities inherent to photonic circuits. This approach provides a pathway toward fastpaced and high-quality solutions for difficult optimization and search problems.

本研究围绕题为《全并行多智能体光子优化器》的学术手稿展开。
摘要：优化问题是诸多关键跨学科应用的核心研究议题。在传统实现方案中，运算的串行特性会对计算效率施加严苛的限制。本研究探讨了模拟光学计算如何突破这一根本性瓶颈。我们提出了一款光子优化单元（photonic optimizer unit），并配套了相关算法：该算法依托受自然启发的多智能体（multi-agent）协作框架实现存内计算（in-memory computation）。该系统可执行一系列可重构并行矩阵向量运算，这一功能依托光子电路固有的高带宽与多路复用特性得以实现。该方案为解决复杂优化与搜索问题提供了一条获取快速优质解决方案的可行路径。