Reinforcement Learning Quantum Neural Network Hybrids for Real-Time Supply-Chain Security: Methods, Threat Models, and a Research Roadmap

Global supply chains are increasingly automated, instrumented, and interconnected creating opportunities for real-time optimization but also novel, rapidly evolving security threats (tampering, insider fraud, diversion, adversarial manipulation of sensors and models). Reinforcement learning (RL) has emerged as a powerful paradigm for sequential decision making in dynamic supply-chain environments, enabling adaptive routing, anomaly response, and recovery actions. Simultaneously, quantum neural networks (QNNs) and other hybrid quantum-classical components promise richer representations and novel algorithmic primitives that may enhance sample efficiency, combinatorial search, and kernel expressivity in data-scarce or adversarial settings. This paper integrates these two frontiers and presents a comprehensive treatment of RL–QNN hybrid architectures tailored for real-time supply-chain security. We provide (1) formal problem definitions and threat models, (2) theoretical and practical descriptions of hybrid RL–QNN designs (policy/value parameterizations, quantum feature maps, gradient estimation), (3) reproducible training algorithms and pseudocode, (4) evaluation and adversarial robustness frameworks, (5) deployment and MLOps guidance for latency-bound environments, and (6) a detailed research roadmap prioritizing near-term hybrid pilots and longer-term fault-tolerant ambitions. We ground the discussion in recent literature on QNNs, variational quantum algorithms, quantum reinforcement learning, and RL for supply chains (Cerezo et al., 2021; Havlíček et al., 2019; Meyer et al., 2024; Yan, 2022; Correll et al., 2023). Practical recommendations emphasize measurable security outcomes, reproducibility, and interpretable governance.