Managing inventory in large-scale supply chains necessitates quick, cost-effective decisions that adhere to constraints such as storage limits and variable demand. This paper describes a novel hybrid approach that uses both quantum and classical computers to tackle inventory problems structured as Quadratic Unconstrained Binary Optimization (QUBO). Each choice variable, such as restocking a product, is represented in binary form, with related costs and penalties built into the objective function. Business constraints are communicated indirectly using adjustable weights, allowing the model to remain unconstrained while discouraging unrealistic conclusions. Optimization is carried out using both classical methods like simulated annealing and quantum solvers such as D-Wave and QAOA, where a hybrid strategy selects the most viable solution. The results indicate that the hybrid system consistently produces lower total costs and better constraint compliance compared to individual solvers, especially for complex, high-dimensional datasets. To ensure secure, automated execution of these optimized decisions, a blockchain layer is introduced. Inventory actions—such as placing orders or updating stock—are carried out through smart contracts that provide tamper-proof, verifiable enforcement. By employing a lightweight Proof of Authority (PoA) mechanism, transaction validation remains fast while maintaining transparency across all nodes in the network. This integration of quantum optimization with blockchain automation enables a responsive and trustworthy framework for modern inventory management.
Managing inventory in large-scale supply chains necessitates quick, cost-effective decisions that adhere to constraints such as storage limits and variable demand. This paper describes a novel hybrid approach that uses both quantum and classical computers to tackle inventory problems structured as Quadratic Unconstrained Binary Optimization (QUBO). Each choice variable, such as restocking a product, is represented in binary form, with related costs and penalties built into the objective function. Business constraints are communicated indirectly using adjustable weights, allowing the model to remain unconstrained while discouraging unrealistic conclusions. Optimization is carried out using both classical methods like simulated annealing and quantum solvers such as D-Wave and QAOA, where a hybrid strategy selects the most viable solution. The results indicate that the hybrid system consistently produces lower total costs and better constraint compliance compared to individual solvers, especially for complex, high-dimensional datasets. To ensure secure, automated execution of these optimized decisions, a blockchain layer is introduced. Inventory actions—such as placing orders or updating stock—are carried out through smart contracts that provide tamper-proof, verifiable enforcement. By employing a lightweight Proof of Authority (PoA) mechanism, transaction validation remains fast while maintaining transparency across all nodes in the network. This integration of quantum optimization with blockchain automation enables a responsive and trustworthy framework for modern inventory management.
A quantum-enabled blockchain architecture for secure and efficient supply chain management is created by combining quantum optimization, decentralized transaction control, and post-quantum security enforcement. Supply chain decision factors such as supplier allocation, routing optimization, and inventory coordination are framed as combinatorial optimization problems and solved using the Quantum Approximate Optimization Algorithm. The optimum decision outputs are carried out via blockchain-based smart contracts, which enable automatic validation, transparent processing, and immutable transaction storage. A quantum-resistant security layer allows for safe authentication, encrypted communication, and attackresistant transaction verification against both classical and quantum adversaries. A multi-tier simulated supply chain environment is used to evaluate system performance, which includes measures such as latency, throughput, optimization accuracy, security resilience, and operational cost. When compared to traditional supply chain and blockchain-based implementations, the results show faster convergence, lower transaction latency, more security, and greater transparency. The findings support the efficacy of combining quantum optimization, decentralized ledgers, and quantum-resilient encryption for next-generation intelligent supply chains.
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.
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.
This paper introduces a groundbreaking hybrid quantum-classical algorithm for the socioeconomically weighted warehouse facility location problem, addressing critical challenges in modern supply chain optimization. We present a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation with rigorously derived penalty parameters, automated constraint repair mechanisms, and theoretically proven performance guarantees. Our hybrid approach integrates demand-weighted kmedoids clustering with quantum annealing, achieving an 7.92% reduction in total weighted distance over classical baselines while maintaining 95% feasibility rates. Key innovations include: (1) mathematically rigorous penalty parameter bounds ensuring constraint satisfaction, (2) automated quantum solution repair algorithms with bounded objective deterioration, (3) intelligent hybrid merging strategies leveraging complementary classicalquantum strengths, and (4) empirical 95% repair bound and a sufficient computable penalty condition. Experimental validation on real Hyderabad socioeconomic data demonstrates superior performance with approximation ratio 1.11 compared to pure quantum approaches (1.22) and classical methods (1.05), establishing practical quantum advantage for logistics optimization,
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.
In highly competitive markets, optimal price setting is a key strategic lever, particularly when item demand is elastic and interrelated. Small changes in the price of one item can significantly influence the demand for others through cross-elasticities, making the search for profit-maximizing price combinations a computationally challenging NP-hard problem. This paper proposes a hybrid quantum–classical approach to address large-scale price optimization under elastic demand with cross-item dependencies. The method combines quantum annealing, executed on D-Wave’s adiabatic quantum computing platform, with classical graph-based clustering to partition the problem into smaller subproblems compatible with current quantum hardware constraints. Each subproblem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model that maximizes total profit while enforcing pricing constraints and incorporating cross-elasticity effects. The system was validated on synthetic datasets simulating item catalogs of up to 2,500 items, with varying price ranges, profit margins, and elasticity densities. Results show that the hybrid solver can deliver high-quality solutions in operationally reasonable times, outperforming pure quantum execution and achieving a substantially more favorable scalability–efficiency trade-off than classical approaches such as exhaustive search and simulated annealing. The proposed framework demonstrates the feasibility of applying quantum annealing to real-world decision-support problems in the near term, even under the technological limitations of current quantum devices. This work anticipates future enhancements in quantum hardware and provides a foundation for integrating quantum optimization into dynamic pricing strategies, allowing organizations to periodically and adaptively recalculate prices as demand patterns and market conditions evolve in commerce and e-commerce environments.