Scalable Parallel Optical Ising Machine for Solving Large‐Scale Combinatorial Optimization Problems

ABSTRACT

Many real‐world applications can be categorized as combinatorial optimization problems (COPs), yet their inherent NP‐hardness and exponential computational complexity pose significant barriers to efficient solutions. Designed specifically for solving COPs, the Ising machine (IM) demonstrates superior performance over traditional Von Neumann‐based computing architectures. Among different IM implementations, optical IMs (OIMs) are especially promising due to their inherent high‐speed and energy efficiency properties. Nevertheless, current OIMs are constrained by serial processing architectures, posing significant challenges for solving large‐scale COPs. To address this challenge, we propose and experimentally demonstrate a multiplexed parallel OIM architecture spanning the optical and electrical domains. The system solved a square lattice MAX‐CUT instance with over 100,000 spins and achieved a fourfold speedup with four‐channel parallelism. In addition, channel‐wise calibration mitigated device mismatch and aligned the nonlinear responses across channels, enabling the four‐channel system to maintain solution quality comparable to the single‐channel system across small benchmark problems of varying difficulty. We also explored a method combining hierarchical clustering and noise injection, which identified the known optimal route within the clustering‐constrained search space of the 14‐city TSP. These results demonstrate the potential of the proposed architecture for larger and more complex optimization problems.