AIBullisharXiv – CS AI · Jun 96/10
🧠
Optimizing Energy-based Neural Network Training with Coherent Ising Machine
Researchers demonstrate a Coherent Ising Machine (CIM) trained to optimize energy-based neural networks using Equilibrium Propagation, achieving performance comparable to traditional software implementations. By integrating the Adam optimizer, the approach significantly improves convergence speed and accuracy while scaling across deeper architectures, positioning quantum-inspired analog hardware as a viable platform for energy-efficient AI.