The research paper deals with fundamental vulnerabilities in the international supply chain of perishable products that were revealed during recent crises like the pandemics crisis and trade wars. We suggest one such quantum optimization model in a binary quadratic formulation (BQM) to enhance the cold-chain logistics as it will integrate production nodes, distribution nodes, and retail nodes. Tests were conducted on two scenarios by the model: wheat shipments in the context of the 202021 Black Sea ports shutdown and Chilean salmon exports with the use of a hybrid quantum-classical method (QAOA). There have been up to 15 percent less goods spoiled, 12 percent shorter deliveries, and a decrease of 30 percent in audit expenditures with blockchain-enabled clear traceability. The research also assesses the implications of quantum parameter tuning to the compliance of WTO SPS and Codex Alimentarius standards. This framework shows the promise of quantum tools as the means of constructing reliable regulation-compliant agri-supply chains.
The research paper deals with fundamental vulnerabilities in the international supply chain of perishable products that were revealed during recent crises like the pandemics crisis and trade wars. We suggest one such quantum optimization model in a binary quadratic formulation (BQM) to enhance the cold-chain logistics as it will integrate production nodes, distribution nodes, and retail nodes. Tests were conducted on two scenarios by the model: wheat shipments in the context of the 202021 Black Sea ports shutdown and Chilean salmon exports with the use of a hybrid quantum-classical method (QAOA). There have been up to 15 percent less goods spoiled, 12 percent shorter deliveries, and a decrease of 30 percent in audit expenditures with blockchain-enabled clear traceability. The research also assesses the implications of quantum parameter tuning to the compliance of WTO SPS and Codex Alimentarius standards. This framework shows the promise of quantum tools as the means of constructing reliable regulation-compliant agri-supply chains.
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.
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.
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.
A multi-objective logistics optimization problem from a real-world supply chain is formulated as a Quadratic Unconstrained Binary Optimization Problem (QUBO) that minimizes cost, emissions, and delivery time, while maintaining target distributions of supplier workshare. The model incorporates realistic constraints, including part dependencies, double sourcing, and multimodal transport. Two hybrid quantum-classical solvers are proposed: a structure-aware informed tree search (IQTS) and a modular bilevel framework (HBS), combining quantum subroutines with classical heuristics. Experimental results on IonQ's Aria-1 hardware demonstrate a methodology to map real-world logistics problems onto emerging combinatorial optimization-specialized hardware, yielding high-quality, Pareto-optimal solutions.
Abstract: Modern supply chains face intricate challenges demanding sophisticated optimization strategies.This research presents a pioneering approach, combining quantum computing and machine learning, to revolutionize supply chain optimization. A hybrid framework harmonizes the power of quantum computing, including quantum annealers and gate-based quantum devices, with classical machine learning techniques. This synthesis aims to surmount the limitations of conventional optimization methods. Through rigorous experimentation on real-world supply chain scenarios, our approach demonstrates substantial efficiency gains and enhanced solution quality in comparison to classical methods. The implications are far- reaching, promising substantial cost reductions, streamlined resource allocation, and sustainability improvements for industries relying on supply chain management. Our study contributes to the burgeoning field of quantum machine learning and offers immediate value to supply chain professionals grappling with complexity. As supply chains evolve, our research sets the stage for innovative solutions in the quest for optimization.
The study presents the earliest effort to conceptualize SCM integration with quantum computing principles, providing dynamic capabilities for SCM. It proposes a unique interdisciplinary framework that demonstrates where hybrid quantum-classical approaches can realise value, and specifies the conditions that organizations must meet to adopt such methods. In this article, a conceptual framework has been proposed for HQCSCO. This framework facilitates key decision-making areas such as inventory optimisation, production scheduling, and logistics routing, where quantum-enhanced algorithms could supplement classical models to enhance performance. At the managerial level, the framework helps manufacturing leaders to determine viability, prioritize high-impact use cases, and develop future-ready digital capabilities that enhance cost efficiency, agility, and resilience. The research provides a conceptually-based baseline of quantum-facile supply chains and offers a research agenda to investigate the strategic, ethical, and operational implications thereof.