The complexity of global supply chains places immense cognitive demands on human planners, creating a high-pressure environment where optimal decision-making is paramount yet increasingly difficult to achieve. This research addresses the central challenge of how to leverage quantum computing (QC) and artificial intelligence (AI) to augment human capabilities in solving complex, NP-hard logistical problems that are often intractable for human planners alone. It contrasts the theoretical underpinnings of hybrid quantum-classical algorithms with the practical, empirically verified results from an industrial case study of an AI-driven logistics solution at Malaysian Airlines' MAB Cargo. The analysis demonstrates that while a generalized 'quantum advantage' remains a long-term goal, significant value is created today through hybrid systems that function as collaborative partners for human operators. In this model, AI excels at structuring complex data and managing operational workflows, while quantum processors tackle specific, computationally intensive sub-problems, presenting optimized solutions to their human counterparts. The findings suggest that the most viable path forward is a synergistic human-machine integration, where quantum-AI components handle the heavy computations, thereby elevating the human role from tedious manual calculation to strategic oversight and exception management.
The complexity of global supply chains places immense cognitive demands on human planners, creating a high-pressure environment where optimal decision-making is paramount yet increasingly difficult to achieve. This research addresses the central challenge of how to leverage quantum computing (QC) and artificial intelligence (AI) to augment human capabilities in solving complex, NP-hard logistical problems that are often intractable for human planners alone. It contrasts the theoretical underpinnings of hybrid quantum-classical algorithms with the practical, empirically verified results from an industrial case study of an AI-driven logistics solution at Malaysian Airlines' MAB Cargo. The analysis demonstrates that while a generalized 'quantum advantage' remains a long-term goal, significant value is created today through hybrid systems that function as collaborative partners for human operators. In this model, AI excels at structuring complex data and managing operational workflows, while quantum processors tackle specific, computationally intensive sub-problems, presenting optimized solutions to their human counterparts. The findings suggest that the most viable path forward is a synergistic human-machine integration, where quantum-AI components handle the heavy computations, thereby elevating the human role from tedious manual calculation to strategic oversight and exception management.
Logistics optimization and operations management increasingly depend on artificial intelligence and quantum computing to address complex, time-sensitive planning problems. Existing research has focused mainly on algorithmic benchmarks or speculative quantum advantage, while offering limited insight into how Hybrid Quantum AI creates organizational value under current hardware constraints. This study advances theory by integrating Information Processing Theory (IPT) with the Dynamic Capabilities View (DCV), demonstrating how Hybrid Quantum AI architectures generate organizational value despite the current absence of quantum advantage. Using a case analysis of Malaysia Airlines Cargo (MAB Kargo), it is shown how combining AI-driven data structuring with quantum-enhanced optimization can close critical information-processing gaps and support the development of sensing, seizing, and transforming capabilities. Empirically, this hybrid system achieved an 83% reduction in planning time and a 5% increase in capacity utilization. These findings demonstrate that the current value of quantum computing in logistics lies not in outperforming classical solvers, but in enabling modular, future-ready architectures that improve operational fit today and build strategic agility over time. The study contributes to management and information systems research by explaining “quantum readiness” as an organizational capability enabled by modular digital infrastructure, offering practical guidance for digitization roadmaps and computational investment decisions in next-generation logistics.
This chapter explores the transformative potential of Quantum Artificial Intelligence (QAI) in driving significant cost savings across various business operations. As organizations face increasing pressure to optimize resources and enhance operational efficiency, traditional computational methods often fall short in addressing complex, large-scale problems. Quantum AI, which synergizes the computational power of quantum computing with advanced machine learning techniques, presents a promising solution. This chapter delves into the theoretical underpinnings of QAI, showcases real-world applications in logistics, supply chain, finance, and manufacturing, and provides a comparative analysis of cost efficiencies achieved through QAI-driven optimizations. Case studies and empirical evidence highlight how QAI not only accelerates decision-making but also minimizes operational redundancies, ultimately leading to substantial financial benefits. The chapter concludes with future directions and challenges in the adoption of QAI technologies in mainstream business practices.
This research paper analyses how quantum computing and artificial intelligence (AI) could be used to enhance resource utilisation within the cargo industry. Because more challenges are emerging in logistics and supply chain management within the sector, quantum computing has promising possibilities for improving AI techniques. The paper describes a survey carried out among industry professionals to identify the key challenges involved in real-time decisions and inventory, and to investigate the industry’s perception of quantum-AI’s effectiveness and sustainability. The results show that existing applications such as route optimisation and predictive analytics are the preferred solutions and hold significant promise for maximising business operational efficiencies and minimising carbon impacts. It is expected that adoption will be reached in 2–5 years; however, concerns including scalability issues and rigidity of workforce present major hurdles. The paper aims to shed light on the logistics industry’s future revolution in quantum computing and discusses some of the ways whereby the challenges can be addressed and sustainable development achieved.This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Quantum computing presents a transformative opportunity for optimizing supply chain and logistics operations by addressing complex combinatorial challenges beyond the capabilities of classical computing. Traditional supply chain optimization relies on heuristic and mathematical models, which struggle with large-scale problems such as vehicle routing, warehouse management, demand forecasting, and last-mile delivery. Quantum computing, leveraging qubits and principles like superposition and entanglement, enables exponential computational power and efficiency. This paper explores quantum computing's applications in logistics, including quantum annealing for route optimization, fleet management, and supply chain network design. Companies like IBM, Google, and D-Wave are advancing quantum algorithms, with hybrid quantum-classical approaches emerging as practical solutions. While large-scale commercial adoption is still in its infancy, research demonstrates promising results in reducing operational costs, improving sustainability, and enhancing decision-making. The study synthesizes over 80 published works on quantum computing's role in logistics, providing insights into emerging technologies, challenges, and future potential. As the field progresses, early adoption and pilot programs will be crucial for businesses to gain a competitive edge in an increasingly complex global supply chain landscape. Quantum computing has the potential to redefine efficiency, cost reduction, and real-time adaptability in logistics and supply chain management.
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.
Artificial Intelligence (AI) and quantum computing are converging to form a new era of ultra-fast computational problem-solving. Quantum optimization algorithms—when integrated with AI-driven heuristics, reinforcement learning, and neural-guided search models—offer unprecedented capabilities for tackling complex problems such as NP-hard optimization, logistics scheduling, cryptographic analysis, and large-scale simulation tasks. This research investigates the combined potential of AI-augmented quantum algorithms, focusing on how machine learning enhances qubit efficiency, error mitigation, quantum annealing performance, and adaptive gate-level optimization. A practical case study evaluates hybrid AI-quantum models applied to combinatorial optimization tasks, demonstrating significant reductions in computational time and improved solution accuracy. Findings highlight a future in which AI-enhanced quantum systems redefine computation, enabling breakthroughs in science, industry, and national innovation systems