Quantum-Inspired RL Optimizes Secure Sustainable Supply Chains

A Jan. 29, 2026 arXiv preprint presents a quantum-inspired reinforcement learning framework that integrates a controllable spin-chain analogy with AIoT signals to jointly optimize carbon footprint, inventory, and cryptographic-like security measures. In simulation the method shows smooth convergence, robust late-episode performance, and resilience to noise, outperforming standard learned and model-based baselines. The work suggests potential for secure, eco-conscious supply chain automation.