Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems

TL;DR

This work addresses secure and sustainable AIoT-driven supply chains by introducing a quantum-inspired reinforcement learning framework that maps inventory, security, and carbon objectives onto a controllable spin-chain model. It formalizes the problem as a multi-objective fidelity-security-emissions MDP and proves controllability and convergence results for an ensemble RL approach, including robustness to bounded quantum noise. The method embeds quantum control concepts with real-time AIoT signals, using a DQN-PPO ensemble to learn near-optimal policies that balance quantum fidelity, cyber-security, and environmental impact, outperforming both traditional RL and model-based controllers. The findings demonstrate stable training, strong late-episode performance, and resilience to noise, outlining a pathway toward secure, eco-conscious, large-scale AIoT supply-chain operations, with future work focusing on hardware integration and more nuanced noise models.

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 framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures. We design a quantum-inspired reinforcement learning framework that couples a controllable spin-chain analogy with real-time AIoT signals and optimizes a multi-objective 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.

Figures (7)

• Figure 1: Three primary components of our quantum-inspired RL approach: (1) the Policy (Agent) selects actions, (2) the Reinforcement Learning Algorithm updates Q-network parameters, and (3) the Environment provides the next state and reward.
• Figure 2: Conceptual XY spin chain with $N=3$. Three spin sites ($n{=}1,2,3$) are coupled by nearest-neighbor XY interactions ($J$), with local control fields $B_n(t)$ applied along $z$; noise is indicated schematically.
• Figure 3: LR sweep across six experiments. Episode rewards (moving average over 10 episodes) for the proposed method versus PPO, DDQN, ACKTR, DDPG, Ensemble RL, and ACER under six learning rates (0.005 to 0.0001). Lower LRs yield more stable convergence; the best peak and late-episode averages occur at LR${}=10^{-4}$. Horizontal dashed lines mark GRAPE, MPC, and Human references.
• Figure 4: Ablation over number of spins $N$. Left: bar chart of Best (Max of MA) and Mean (of MA) as $N$ varies. Right: convergence curves (10-episode MA) showing learning stability. $N{=}3$ achieves the highest best and mean rewards, with robust, fast convergence; larger $N$ degrades both metrics.
• Figure 5: Sensitivity to reward coefficients $(\alpha_1,\alpha_2,\alpha_3)$. Mean reward, final-10-episode average, and best episode across eight configurations. The best configuration is $(1.0,\,1.0,\,0.5)$, balancing fidelity and security with a moderate emissions penalty; increasing $\alpha_3$ reduces reward as expected.

Theorems & Definitions (13)

• Lemma 1: Rank Condition for Controllability
• proof : Proof
• Lemma 2: Approximate Reachability Under Noise
• proof : Proof
• Theorem 1: Well-Posedness and Ensemble RL Solution
• proof
• Definition 1: Well-Defined MDP
• Definition 2: Ensemble RL Framework
• Definition 3: Solvability and RL Convergence
• Theorem 2: $\epsilon$-Optimal Convergence