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/.
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/.
This study examines how quantum computing, quantum algorithms, and AI-quantum hybrid models enhance logistics efficiency and sustainability in e-commerce. Logistics optimization is analyzed to improve routing, scheduling, and resource allocation. The mixed-method design combines a cross-sectional survey of professionals with semi-structured interviews. Quantitative data were analyzed using structural equation modeling in SmartPLS, and qualitative data were thematically assessed. A perception-based analysis examined how professionals perceive quantum-based logistic models compared to traditional AI-driven approaches. Professionals believe that these models can enhance logistics optimization, increasing efficiency and sustainability. Respondents perceived that quantum models could outperform AI-driven approaches, particularly in routing and freight scheduling, but highlighted high implementation costs, limited expertise, and cross-industry collaboration. Logistic optimization mediates the relationship between quantum technology and performance outcomes. This study provides empirical evidence on industry perceptions and strategic guidance for firms considering quantum logistics. Quantum-enabled logistics enhance operational performance and support global sustainability goals. The findings underscore the opportunities and challenges of quantum logistics, offering guidance for research and adoption strategies.
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.
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.
This chapter explores how quantum computing can revolutionise the quick commerce industry, focusing on logistics and supply chain management to boost efficiency and sustainability. Quick commerce, an emerging trend in e-commerce, promises incredibly fast delivery speeds to satisfy ever-growing consumer expectations. But this rapid expansion isn’t without its hurdles, particularly when it comes to maintaining smooth operations and being eco-friendly. Quantum computing steps in as a potential game-changer, bringing its powerful processing abilities to the table. Integrating quantum computing into quick commerce could transform logistics operations, from planning delivery routes to managing warehouse resources. It's not just about speeding things up; it's about rethinking the entire supply chain, including how we handle inventory and the final leg of delivery. Quantum algorithms, which are built on the principles of quantum mechanics, can help companies predict demand more accurately, restock shelves faster, cut down on waste, and enhance overall efficiency. These algorithms are especially good at optimising routes in real time, considering various factors to ensure quicker, more dependable deliveries. This study aims to bridge the gap between the theory and practice of quantum computing in logistics. It examines how quantum computing can be used, its possible benefits, and the challenges it might face in the quick commerce sector. The chapter argues that quantum computing could usher in a new era of logistics management characterised by unprecedented efficiency in routing deliveries, controlling inventory, and allocating resources. Highlighting the use of quantum algorithms for dynamic routing and demand forecasting underscores the potential for creating a more agile and eco-friendly delivery system. Ultimately, this research shines a light on how we can turn the conceptual promise of quantum computing into real-world improvements in quick commerce logistics, advocating for a future where quantum computing leads to a sustainable and efficient quick commerce ecosystem.
Quantum computing, which has the ability to solve the most challenging logistics and supply chain management problems, is the most fascinating advancement in computation for the ensuing ten years. The current state of quantum computing is reviewed in this study, along with recommendations for further research. The transformational potential of quantum 68 computing is explored in this study within the framework of supply chain management optimization. The necessity for cutting-edge computational tools grows more and more important as supply networks get more complex. With its capacity to analyze huge datasets and resolve challenging optimization issues, quantum computing presents a promising way to transform supply chain operations. This study examines how quantum computing might improve supply chain efficiency, address the complex problems associated with contemporary supply chains, and provide significant value to both enterprises and consumers. First, fundamental ideas related to quantum computing and computers are introduced. The dominant quantum technologies are then described. The third section includes instances of contemporary supply chain management and logistical advancements as well as a study of the quantum sector. Fourth, suggestions for further investigation are given. This review intends to inform and motivate the application of quantum computing to supply chain and logistics optimisation, machine learning, and artificial intelligence.
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.