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.
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.
Sustainable supply chains are becoming increasingly important as businesses strive to reduce environmental impact while maintaining efficiency and profitability. One emerging solution is Quantum Intelligence (Quantum AI)—a powerful combination of quantum computing and artificial intelligence that can handle highly complex and data-intensive supply chain problems. Quantum AI leverages unique quantum principles such as superposition and entanglement to explore a vast number of possible solutions simultaneously. This enables better decision-making in areas like production scheduling, inventory management, and emissions forecasting. As a result, organizations can optimize delivery routes, reduce operational costs, and significantly lower carbon emissions, contributing to more sustainable and environmentally responsible supply chains. A key approach in this field is the hybrid quantum-classical model, which combines the data-processing strengths of classical computing with the advanced computational capabilities of quantum systems. This hybrid approach helps overcome current limitations in quantum hardware, such as noise, limited qubits, and high error rates, making Quantum AI more practical for real-world applications. In addition, integrating technologies like the Internet of Things (IoT), blockchain, and digital twins further enhances supply chain performance. IoT devices provide real-time data from sensors, blockchain ensures transparency and traceability, and digital twins allow simulation of supply chain operations to predict environmental impacts and test different scenarios. Overall, Quantum AI represents a promising step toward building smarter, more efficient, and sustainable supply chains that can adapt to modern challenges while supporting global environmental goals.
AI-augmented quantum computers have the potential to revolutionise logistics and supply chain operations by facilitating intelligent, responsive, and sustainable management processes. Contemporary supply chains are characterised by complexity, integration, globalisation, and dynamism necessitating instantaneous decision-making that surpasses traditional optimisation and forecasting capabilities. Artificial intelligence applications such as machine learning algorithms, predictive analytics, and generative AI models have contributed to improved demand forecasting, inventory management, and route optimisation enhancing supply chain resilience and visibility. However, challenges persist in tackling large-scale combinatorial optimisation problems required for intricate logistics networks using classical computational systems. Quantum computing offers the potential to address these challenges by enabling superior optimisation scheduling and resource allocation through hybrid quantum–classical approaches. The convergence of AI and quantum technologies enables real-time analytics, carbon-aware route optimisation, and autonomous decision-making capabilities for designing sustainable and resilient logistics networks.
The relationship between quantum computing and the Internet of Things (IoT) is becoming a powerful pathway to develop secure, intelligent and sustainable network systems – however, present-day IoT architectures experience scalability challenges, as well as energy and data vulnerability, due to classical models in cryptography. This paper presents a Next Generation Quantum-IoT Communication Framework (QIoT-CF) that addresses these issues with quantum-based encryption, entanglement-assisted routing, and dynamic sustainability optimization for communication in the framework. The architecture uses Quantum Key Distribution (QKD) to ensure quality data security that is unbreakable, and it uses reinforcement learning–based queuing to balance energy consumption and communication reliability with heterogeneous IoT nodes. The simulations suggest that latency decreased by 45%, energy consumption improved by 43%, and the quantum bit error rate (QBER) improved by 52% relative to current IoT systems. The sustainability controller will change transmission policies based on carbon-aware energy metrics providing an acceptable trade-off between quantum performance and sustainability at-scale. The framework represents a critically significant advancement toward eco-intelligent quantum communication security situated within the 6G vision and beyond.
Quantum communication networks are a revolutionary approach to secure data transmission using quantum mechanics like entanglement and quantum key distribution. Nonetheless, the evolution of the quantum network infrastructure creates substantial problems related to secure data transmission, energy reduction, and efficient management of scarce quantum resources. The inefficiency of traditional approaches that show little flexibility regarding quantum channel dynamics and different traffic patterns leads to low communication efficiency and high operational cost. To address these limitations, this study proposes a Quantum-Inspired Energy-Aware Secure Deep Reinforcement Optimization (QESDRO) framework. The proposed method is combining the quantum-inspired evolutionary optimization with the Deep Reinforcement Learning (DRL) to achieve intelligent routing, adaptive resource allocation, and energy-aware security management in quantum communication networks. Quantuminspired operators is improving global exploration, while the DRL agent is learning optimal routing and scheduling policies, which is based on channel fidelity, energy availability, congestion levels, and security risks. A multi-objective reward mechanism simultaneously maximizes quantum transmission fidelity, minimizes latency and energy consumption, and is strengthening communication resilience against the malicious attacks. Experimental evaluation is showing that QESDRO is achieving 98.9% communication reliability, 97.5% secure routing accuracy, and 96.6% resource utilization efficiency, while it is reducing energy consumption to 52 J and transmission latency to 18 ms when it is compared with the Conventional optimization approaches. These results are confirming the effectiveness of the proposed framework in simultaneously enhancing communication security, operational efficiency, and energy-aware resource management for the next-generation quantum communication networks.
The demands from customers, uncertainty of inventory, fluctuations in supply by the vendors, warehouse constraints, and transportation issues have made the retail supply chains increasingly complex. The optimization problem in large-scale and dynamic supply chains is challenging for conventional optimization methods to efficiently obtain solutions with small solution quality and computational time. To address those drawbacks, an intelligent optimization approach of intelligent retail supply chain management using the quantum-inspired Algorithm (QIA) is proposed in this paper. The proposed framework integrates the activities of forecasting demand, optimizing inventory levels, supplier selection, warehouse allocation, transportation scheduling, and distribution planning into a decision-making model. Such search mechanisms are quantum-inspired and are applied to efficiently search through a vast solution space of a complex problem and to locate near-optimum resource allocation strategies among a set of operational constraints. Furthermore, it contains an adaptive feedback mechanism, which adapts optimization decisions on the fly based on the evolving supply chain situation, allowing it to respond to changes in demand and operational disruptions. The proposed framework is measured by several performance indicators such as inventory utilization, transportation efficiency, order fulfillment rate, computational convergence, and the operational cost. Experimental results demonstrate the stability of the optimization process, the quick convergence, and the efficient use of the resources, achieving an overall classification accuracy of 98.1%. The proposed framework provides a scalable, efficient, and smart solution to optimize the modern retail supply chain, thereby enabling operational efficiency, cost reduction in logistics, enhanced decision-making capability, and resilient and sustainable retail supply chain operations.
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.