Technology Category
Machine Learning: Quantum Machine LearningSearch and Optimization: Learning to SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems
🤖 AI Summary
This work proposes a quantum-inspired multi-objective reinforcement learning framework for supply chain optimization that explicitly integrates sustainability and cybersecurity—dimensions often overlooked in existing models. By drawing an analogy between spin chains in quantum physics and sequential decision-making in supply chains, the approach leverages real-time AIoT data, ensemble learning, and window normalization to construct a unified reward mechanism balancing inventory efficiency, carbon footprint reduction, and encryption-like security strategies. Experimental results demonstrate that the proposed method achieves stable convergence and strong robustness, significantly outperforming both learning-based and model-driven baselines under noisy conditions. The framework thus enables synergistic optimization of secure, low-carbon, and efficient logistics operations.
Technology Category
Application Category
📝 Abstract
Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled Internet of Things (AIoT) solutions for global commerce. However, conventional supply chain optimization models often overlook crucial sustainability goals and cyber vulnerabilities, leaving systems susceptible to both ecological harm and malicious attacks. To tackle these challenges simultaneously, this work integrates a quantum-inspired reinforcement learning (RL) framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures. We design a quantum-inspired RL framework that couples a controllable spin-chain analogy with real-time AIoT signals and optimizes a multiobjective reward unifying fidelity, security, and carbon costs. The approach learns robust policies with stabilized training via value-based and ensemble updates, supported by window-normalized reward components to ensure commensurate scaling. In simulation, the method exhibits smooth convergence, strong late-episode performance, and graceful degradation under representative noise channels, outperforming standard learned and model-based References, highlighting its robust handling of real-time sustainability and risk demands. These findings reinforce the potential for quantum-inspired AIoT frameworks to drive secure, eco-conscious supply chain operations at scale, laying the groundwork for globally connected infrastructures that responsibly meet both consumer and environmental needs.
Problem
Research questions and friction points this paper is trying to address.
sustainable supply chain
cybersecurity
carbon footprint
AIoT
supply chain optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
quantum-inspired reinforcement learning
AIoT-driven supply chain
multi-objective optimization
spin-chain analogy
sustainable and secure AI
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