Abstract Supply chains face increasingly frequent and severe disruptions, while the strategic interactions among suppliers, manufacturers, logistics providers and retailers are traditionally modelled with classical, non-cooperative game theory that struggles to capture correlated, non-classical strategic correlations and scales poorly on the combinatorial optimization problems that resilience investment decisions induce. This paper proposes an integrated \emph{Quantum Game-Theoretic Optimization} framework for resilient supply chains under AI-driven disruption management. The framework combines (i) an Eisert--Wilkens--Lewenstein (EWL) quantum game formulation of the four-agent resilience-investment game, (ii) a Quantum Approximate Optimization Algorithm (QAOA) solver for the resulting resilience-allocation QUBO, augmented with a novel \emph{Ramanujan-inspired regularization} term built from the Hardy--Ramanujan partition asymptotic and Ramanujan summation (zeta-regularization), and (iii) a multi-agent reinforcement learning (MARL) layer whose learning-rate schedule is modulated by the Rogers--Ramanujan continued fraction. We formally derive the entangled state evolution, the Ising/QUBO mapping, the QAOA cost and mixer Hamiltonians, the Ramanujan regularization term, and the MARL update rule, and we present a five-layer reference system architecture. Using exact statevector simulation (no physical quantum hardware required), we address five research questions spanning equilibrium payoffs under entanglement, QAOA depth scaling, the effect of Ramanujan regularization on optimization-landscape stability, MARL performance under varying disruption severity, and an overall multi-metric benchmark against classical game theory, classical mixed-integer programming, and classical MARL baselines. Simulation results show that entanglement can raise total equilibrium payoff substantially above the classical (product-state) Nash equilibrium by escaping mutually-defecting ''no-investment'' traps, that QAOA approximation ratio improves with circuit depth up to a problem-dependent saturation point, that Ramanujan regularization yields a smoother but instance-dependent optimization landscape, and that the integrated framework achieves the highest service level and robustness score among five compared methods, at a moderate convergence-time cost. Complete derivations, a reproducible open-source Python implementation, and all simulation data are provided.
Abstract Supply chains face increasingly frequent and severe disruptions, while the strategic interactions among suppliers, manufacturers, logistics providers and retailers are traditionally modelled with classical, non-cooperative game theory that struggles to capture correlated, non-classical strategic correlations and scales poorly on the combinatorial optimization problems that resilience investment decisions induce. This paper proposes an integrated \emph{Quantum Game-Theoretic Optimization} framework for resilient supply chains under AI-driven disruption management. The framework combines (i) an Eisert--Wilkens--Lewenstein (EWL) quantum game formulation of the four-agent resilience-investment game, (ii) a Quantum Approximate Optimization Algorithm (QAOA) solver for the resulting resilience-allocation QUBO, augmented with a novel \emph{Ramanujan-inspired regularization} term built from the Hardy--Ramanujan partition asymptotic and Ramanujan summation (zeta-regularization), and (iii) a multi-agent reinforcement learning (MARL) layer whose learning-rate schedule is modulated by the Rogers--Ramanujan continued fraction. We formally derive the entangled state evolution, the Ising/QUBO mapping, the QAOA cost and mixer Hamiltonians, the Ramanujan regularization term, and the MARL update rule, and we present a five-layer reference system architecture. Using exact statevector simulation (no physical quantum hardware required), we address five research questions spanning equilibrium payoffs under entanglement, QAOA depth scaling, the effect of Ramanujan regularization on optimization-landscape stability, MARL performance under varying disruption severity, and an overall multi-metric benchmark against classical game theory, classical mixed-integer programming, and classical MARL baselines. Simulation results show that entanglement can raise total equilibrium payoff substantially above the classical (product-state) Nash equilibrium by escaping mutually-defecting ''no-investment'' traps, that QAOA approximation ratio improves with circuit depth up to a problem-dependent saturation point, that Ramanujan regularization yields a smoother but instance-dependent optimization landscape, and that the integrated framework achieves the highest service level and robustness score among five compared methods, at a moderate convergence-time cost. Complete derivations, a reproducible open-source Python implementation, and all simulation data are provided.
E-commerce platforms face ever more complex dimensional SKU portfolios, demand volatility, returns, seasonality and cost pressures. Traditional optimization approaches (integer programming, heuristics, meta-heuristics) increasingly strain under scale, real-time demands and combinatorial explosion. Meanwhile, quantum-inspired optimization leveraging methods such as quantum annealing, coherent Ising machines, variational circuits and QUBO (Quadratic Unconstrained Binary Optimization) mapping offer near-term practical approaches to combinatorial logistics and inventory problems. This paper proposes a comprehensive framework for applying quantum-inspired optimization to e-commerce supply-chain logistics and inventory management: we provide an extended literature review of classical and quantum-inspired methods; we derive full mathematical formulations for logistics/inventory problems and map them into QUBO/quantum-inspired frameworks; we design a benchmarking/implementation methodology; we discuss industry architecture and deployment issues (data pipelines, latency, hybrid classical/quantum-inspired workflows, interpretability, cost benefit); we present use cases and discuss practical adoption in ecommerce. Our findings suggest that while fully fault-tolerant quantum computing remains some years away, quantum-inspired optimization can deliver meaningful improvements in logistics and inventory decisions today, particularly for large-SKU e-commerce, multi-warehouse networks and time-sensitive delivery.
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.
Resilience of the supply chain has turned out to be a critical national concern due to the revelations of vulnerabilities in the global networks of logistics that cannot be sufficiently managed by traditional optimization tools. Artificial intelligence and quantum computing can provide revolutionary answers to complex supply chain challenges, addressing them using new computational paradigms. Quantum algorithms use superposition and entanglement to search a solution space that is exponentially larger than the space available to classical computers, and Ireland enables optimal resource allocation in large networks. Hybrid quantum-classical systems are a strategic allocation of computational workload where quantum processors are processing combinatorial optimization kernels and classical systems are doing data preprocessing and solution validation. The quantum algorithms used to optimize the transportation network during disruption conditions are useful in optimizing the network due to the presence of multiple routing strategies in various circumstances of failures. Manufacturing coordination deals with the issue of production scheduling, inventory distribution, and supplier redundancy by quantum formulations that trade off conflicting operational goals. Existing hardware limits put a lower bound on the achievable practical implementation, but the current fast development of qubit quality, connectivity, and error correction implies that a practical quantum advantage soon will be reached in large-scale optimization of supply chains. Quantum computing and supply chain management integration create new principles of ensuring the preservation of critical resource flows in the case of emergencies, which proves the possibilities of increased national resiliency to pandemics, geopolitical conflicts, and natural disasters.
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.
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
The assignment of resources dynamically is a decisive issue in the new paradigms of computations including cloud, edge, fog computing, and the Internet of Things (IoT). These are heterogeneous resource environments, decentralized control environments, and overload environments that vary at a rapid pace and require adaptive and intelligent allocation schemes. The paper suggests a hybrid quantum-game-theoretic model, which combines quantum optimization approaches and multi-agent game theory to solve the problems of multi-agent resource allocation in dynamic systems. Using the quantum concept of superposition and entanglement, the framework can explore the solution spaces in parallel and therefore increase convergence speed and allocation efficiency.A quantum game-theoretic model is developed, which entails price-motivated system to control the strategic interaction of rational agents, in addition to maintaining stability, autonomy and equilibrium. The resource allocation problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solved with the Quantum Approximate Optimization Algorithm (QAOA) algorithm. The proposed approach is proved to be useful as simulation results show significant gains to classical baselines in terms of utility gain, fairness, and system throughput. The results highlight the possibilities of the quantum-game-theoretic paradigm as a fundamental engine of next-generation computing infrastructures, including networked cloud, edge and IoT with networked computing nodes in VPN-enabled networks.
Palanivel