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Scalable Parallel Optical Ising Machine for Solving Large-Scale Combinatorial Optimization Problems
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DOI：10.1002/lpor.71996.png)
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Abstract
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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.
Keywords:
combinatorial optimization
ising machine
ising model
optical device
photonics
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