The rise of e-commerce and calls for sustainability make things hard for businesses in cities, showing where standard shipping methods fail. The study shows a new hybrid quantum-classical optimization scheme that bridges three major gaps: AV-drone coordination that does not work well in the real world; environmental claims that have not been proven; and real-world limits that are not considered enough. In a modern design, the Quantum Approximate Optimization Algorithm (QAOA) and Deep Q-Networks (DQN) are put together. There are two main variables used for optimization: regulatory factors (β =0.32) and energy limits (β =0.56). The framework cuts costs by 28% and speeds up delivery times by 32%, while keeping 89% of solution accuracy on noisy quantum simulations. Run 1,000 Monte Carlo models and compare them to real data to do this. Checking the methods with three steps—computer simulation, stakeholder analysis (n=25, ±=0.81), and policy paper review—is what makes sure they work. The results make it possible to measure the difference in success between models and real life. They show that in the real world, savings of 40% in efficiency drop to 22%. It turns out that regulatory division is the main problem, causing 92% of practical errors. The study produces the idea of energy-constrained quantum advantage and shows that it can only be useful with solid-state batteries (≥400 Wh/kg) and regulatory unification. These efforts set new standards for urban mobility research that involves multiple fields and show how driverless operations can be used in a way that is viable.
The rise of e-commerce and calls for sustainability make things hard for businesses in cities, showing where standard shipping methods fail. The study shows a new hybrid quantum-classical optimization scheme that bridges three major gaps: AV-drone coordination that does not work well in the real world; environmental claims that have not been proven; and real-world limits that are not considered enough. In a modern design, the Quantum Approximate Optimization Algorithm (QAOA) and Deep Q-Networks (DQN) are put together. There are two main variables used for optimization: regulatory factors (β =0.32) and energy limits (β =0.56). The framework cuts costs by 28% and speeds up delivery times by 32%, while keeping 89% of solution accuracy on noisy quantum simulations. Run 1,000 Monte Carlo models and compare them to real data to do this. Checking the methods with three steps—computer simulation, stakeholder analysis (n=25, ±=0.81), and policy paper review—is what makes sure they work. The results make it possible to measure the difference in success between models and real life. They show that in the real world, savings of 40% in efficiency drop to 22%. It turns out that regulatory division is the main problem, causing 92% of practical errors. The study produces the idea of energy-constrained quantum advantage and shows that it can only be useful with solid-state batteries (≥400 Wh/kg) and regulatory unification. These efforts set new standards for urban mobility research that involves multiple fields and show how driverless operations can be used in a way that is viable.
Quantum computing holds transformative potential for optimizing large-scale drone fleet operations, yet its nearterm limitations necessitate hybrid approaches blending classical and quantum techniques. This work introduces Quantum Unmanned Aerial Delivery Routing Optimization (QUADRO), a novel hybrid framework addressing the Energy-Constrained Capacitated Unmanned Aerial Vehicle Routing Problem and the Unmanned Aerial Vehicle Scheduling Problem. By formulating these challenges as Quadratic Unconstrained Binary Optimization problems, QUADRO leverages the Quantum Approximate Optimization Algorithm for routing and scheduling, enhanced by classical heuristics and post-processing. We minimize total transit time in routing, considering payload and battery constraints, and optimize makespan scheduling across various drone fleets. Evaluated on adapted Augerat benchmarks (16-51 nodes), QUADRO competes against classical and prior hybrid methods, achieving scalable solutions with fewer than one hundred qubits. The proposed results underscore the viability of hybrid quantumclassical strategies for real-world drone logistics, paving the way for quantum-enhanced optimization in the Noisy Intermediate Scale Quantum era.
Quantum computing (QC) stands at the cusp of revolutionizing computation, yet its near-term potential, constrained by Noisy Intermediate-Scale Quantum (NISQ) devices, remains underexplored. This dissertation investigates how hybrid quantum-classical algorithms can address combinatorial optimization challenges in logistics, focusing on vehicle routing and drone delivery—NP-hard problems with exponential solution spaces that defy classical exhaustive methods. Amidst NISQ limitations like limited qubits and high noise, we confront key challenges: encoding complex constraints, e.g., time windows, battery capacity, into quantum models, balancing quantum and classical components for scalability, and accessing scarce quantum resources. By integrating quantum annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) with classical heuristics, this work demonstrates QC’s practical utility, advancing both optimization theory and real-world logistics applications. The dissertation unfolds across four core contributions. First, the Hybrid Quantum Tabu Search (HQTS) optimally solves the 50-location Capacitated Vehicle Routing Problem (CVRP), achieving a 4.72% optimality gap. Secondly, we refine HQTS to reach a 2.15% gap with enhanced QA utilization, outperforming prior hybrids on standard datasets. Third, adapting HQTS for the CVRPTWwith D-Wave’s Constrained Quadratic Model solver yields a 3.85% gap on 100-location instances, leveraging a novel heuristic to ensure time-window feasibility where quantum outputs falter. Lastly, QUADRO, a pioneering framework for drone delivery, employs QAOA-driven routing and hybrid scheduling, minimizing transit time and makespan for fleets of 2–11 drones needing fewer than 100 qubits, rivaling classical benchmarks on adapted Augerat datasets. These advances, tested via D-Wave’s Advantage system and Qiskit simulations, showcase hybrid strategies that mitigate NISQ constraints, delivering scalable, feasible solutions to logistics challenges. This work establishes near-term QC’s viability for combinatorial optimization, bridging theoretical promise with practical impact. Synthesizing quantum exploration with classical refinement achieves competitive performance, e.g., matching or surpassing Google ORTools in select CVRPTW cases, while illuminating NISQ limits, such as feasibility degradation beyond small scales. These hybrid strategies deliver scalable, feasible solutions, from a 2.15% optimality gap for CVRP to effective drone fleet management with fewer than 100 qubits, illuminating quantum computing’s potential despite its current constraints. Ultimately, this dissertation positions hybrid QC as a transformative tool for optimization challenges, offering a robust foundation for scaling quantum optimization into operational reality.
Abstract: This paper suggests a new method for improving routes in complicated logistics systems by combining cognitive modeling with quantum computing algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA). In the classic Traveling Salesman Problem (TSP), the model shows major improvements, beating traditional methods by 25% in finding solutions accurately and cutting computation time by 30%. Simulations show a 15% drop in travel time and a 20% cut in CO₂ emissions, highlighting how the model helps improve efficiency and support environmental sustainability. The innovation comes from combining two usually separate fields: cognitive modeling, which mimics how humans make decisions, and quantum computing, which allows for fast and large‑scale optimization. This teamwork between different fields encourages quick, flexible, and scalable decision‑making, which is essential in fast‑changing, real‑time logistics settings. The model matches the move towards Industry 5.0, which focuses on working together with machines and being environmentally friendly. It also supports the United Nations Sustainable Development Goals, especially Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). To make sure the study is valid, it uses open‑access datasets and simulates real‑life situations, such as smart warehouse operations and fleet management systems. The results highlight how quantum‑enhanced cognitive systems can change the game, providing a modern tool to build smarter, greener, and stronger supply chains. This research not only pushes the boundaries of optimization science but also lays the groundwork for using quantum algorithms in industry in the future.
The integration of drones into last-mile delivery operations has the potential to significantly enhance responsiveness and reduce delivery costs. In this paper, we study the Coordinated Truck-Drone Routing Problem (CTDRP), where a truck serves as mobile base station that launches and retrieves a drone to deliver to geographically dispersed customers. Some customers are served by the truck, while others are served by the drone. The objective is to optimize the truck route and drone assignments in order to minimize the total delivery distance, including both ground and aerial travel. We model the CTDRP as a combinatorial optimization problem and solve it using the Quantum Approximate Optimization Algorithm (QAOA), a variational hybrid quantum-classical algorithm that offers promising capabilities for discrete optimization. The proposed method is benchmarked against classical solutions obtained using the Gurobi optimizer, and the results show that QAOA delivers competitive performance. These findings demonstrate the applicability of QAOA in solving integrated aerial-ground logistics problems and support its potential for advancing quantum-enhanced transportation systems.
Quantum computing in drone technology begins to show potential big time, especially in the possibility of data processing, communication, and how to bring operational efficiency.In this study, the integration of quantum algorithms on drone navigation systems to enhance their efficiency in terms of security and performance is being investigated.However, in the interest of full disclosure, it should be noted that quantum-enabled drones have not been built and tested elsewhere to get an insight from a practical standpoint. This research is based on simulations and hypothetical models, which show that quantum algorithms could improve the optimization of drone operations.A simulation performed showed that the mean path length of data transmission was shortened by 12.4%, both from source to destination and back, compacting the whole process and increasing speed while it decreased packet loss by an average value close to 75% — when compared with regular methods. High-density urban obstacle avoidance efficiency increased by 26.1%. Though these results demonstrate an impressive theoretical advantage, realizing practical quantum computing on drones has many challenges to be overcome such as miniaturization of hardware components, power consumption and environmental protection. Overcoming these challenges is critical for future explorations into what quantum-integrated drones can achieve.This article presents a holistic overview of the current status of quantum technological advances in drones and sketches out what constitutes crucial steps to make things turn from theoretical calculations to real-life operational systems. Accordingly, this study sets a new foundation for future advancements in this nascent field and offers solutions that could get one step closer to realizing ambitious visions of autonomous aerial systems across multiple domains.
Optimization problems are widely encountered in real-world domains such as energy systems, financial markets, transportation networks, supply chain logistics, manufacturing processes, and e-commerce platforms. Traditional classical computing methods often struggle to solve large combinatorial optimization problems due to the exponential growth of the solution space. Quantum computing has emerged as a promising approach to address these challenges by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum hardware is limited by noise, restricted qubit availability, and scalability issues, making fully quantum solutions impractical for large-scale applications. To overcome these limitations, this research proposes a hybrid quantum–classical supercomputing framework for solving complex optimization problems across multiple domains. The framework integrates classical data processing and optimization techniques with quantum-inspired algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), to efficiently explore large solution spaces. Real-world datasets from energy systems, supply chain logistics, financial markets, transportation networks, manufacturing processes, and e-commerce platforms are used for experimental evaluation. The results demonstrate that the hybrid optimization approach can generate higher-quality solutions and improved optimization accuracy compared to traditional classical methods. These findings highlight the potential of hybrid quantum–classical systems as an effective and scalable solution for solving complex optimization problems in modern computational applications.