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Quantum-Inspired Reinforcement Learning Shows Carbon Reduction for AIoT Supply Chains

Researchers introduced a quantum-inspired reinforcement‑learning framework that optimises AIoT‑driven supply chains while cutting carbon emissions, improving inventory control, and enhancing cybersecurity. By modelling the supply network as a controllable spin‑chain, the system balances fidelity, security, and emissions within a multi‑objective reward function. Simulated experiments demonstrated smooth convergence, robust performance under realistic noise, and superiority over conventional RL and model‑based approaches. The study provides a theoretical foundation and practical pathway for secure, sustainable logistics at scale.