Wavelength‐Division Multiplexing Scheme Enabling Sparse Spin Interactions For Spatial Photonic Ising Machines

Spatial photonic Ising machines (SPIMs) offer a promising physical computing architecture for solving NP‐hard combinatorial optimization problems. However, when applied to models with sparse spin interactions, conventional SPIMs still employ a fully connected scheme, which demands digital preprocessing to decompose the interaction matrix and encode full spin interactions, leading to massive resource consumption on spatial light modulators (SLMs) when the spin size is large. Here, we propose and experimentally demonstrate a wavelength‐division multiplexing scheme to enable sparse spin interactions for SPIMs, where only the non‐zero spin interactions are encoded by SLMs, bypassing the need for preprocessing computation. Leveraging the non‐Von Neumann architecture of photonic computing, this method reduces the computational complexity of a single‐spin update in Metropolis–Hastings sampling from to . We successfully applied this system to solve the Max‐Cut problem across various sparse graphs, achieving a considerable probability of finding optimal or near‐optimal solutions, with performance comparable to classical simulated annealing. The proposed spatial encoding scheme requires linear resources scaling with the number of connections between spins, providing a highly scalable and energy‐efficient optical accelerator platform for large‐scale sparse combinatorial optimization.