Proper planning is required for arranging cargos of different types, sizes and weights across the aircraft, so that the aircraft can fly safely with lesser fuel consumption and its payload capabilities can be utilized to the fullest, to maximize the revenue. Furthermore, along with the optimal loading, considering minimization of the loading/unloading operations at multiple stopovers adds complexity to the planning. In this paper, we present the quantum approach to address these two real-world challenges faced by the aviation industry, that is, maximizing the payload capacity of the aircraft and minimizing the loading/unloading operations across multiple stopovers. We experimented with both quantum annealing based and gate-based algorithms. As per our findings with respect to the performance and capabilities of the quantum platforms, we found D-Wave to be the most promising one as it supports enough qubits to address a realistic problem as compared to gate-based and quantum inspired optimizers. So further experimentation was carried out using D-Wave on Boeing 747-SF aircraft, one of the largest cargo carriers. Since in this work, our study is restricted to Boeing 747-8F aircraft, we can address different models of cargo aircrafts, transportation of cargo in the lower deck of passenger aircraft as a future scope of work, and work with different modes of transit system such as ships, trucks, rails.
Proper planning is required for arranging cargos of different types, sizes and weights across the aircraft, so that the aircraft can fly safely with lesser fuel consumption and its payload capabilities can be utilized to the fullest, to maximize the revenue. Furthermore, along with the optimal loading, considering minimization of the loading/unloading operations at multiple stopovers adds complexity to the planning. In this paper, we present the quantum approach to address these two real-world challenges faced by the aviation industry, that is, maximizing the payload capacity of the aircraft and minimizing the loading/unloading operations across multiple stopovers. We experimented with both quantum annealing based and gate-based algorithms. As per our findings with respect to the performance and capabilities of the quantum platforms, we found D-Wave to be the most promising one as it supports enough qubits to address a realistic problem as compared to gate-based and quantum inspired optimizers. So further experimentation was carried out using D-Wave on Boeing 747-SF aircraft, one of the largest cargo carriers. Since in this work, our study is restricted to Boeing 747-8F aircraft, we can address different models of cargo aircrafts, transportation of cargo in the lower deck of passenger aircraft as a future scope of work, and work with different modes of transit system such as ships, trucks, rails.
In this submission we solve the Aircraft Loading Optimization problem of the Airbus Quantum Computing Challenge. Finding the optimal loading for a plane is a challenging task for classical algorithms, especially because the solution must respect several flight constraints. The contribution of this work is formulating this problem and its constraints in a model based on QUBO equations which are compatible with quantum annealers. We then benchmarked the model on different solvers to evaluate the performances and capabilities of current technologies.
The aircraft load balancing problem refers to arranging aircraft cargo such that the center of gravity (CoG) is in the optimal position. This problem is high impact in terms of safety and profitability, but is currently solved manually, leading to potentially suboptimal solutions and long lead times. In this study, we investigate the performance of quantum annealing in solving the problem, comparing performance with classical solvers. Our results indicate that for smaller problems, the classical solvers are faster and return higher-quality solutions, but the quantum annealers have more favourable time scaling, which indicates a potential quantum advantage for larger problems. However, for larger problem sizes the annealer fails to return any feasible solution, which indicates that the problem would need to be reformulated to reduce size in order to achieve quantum advantage.
Quantum computing offers transformative potential for addressing complex combinatorial optimization challenges in logistics and supply chain management. This paper presents a comprehensive investigation of quantum annealing and hybrid quantum-classical algorithms applied to unit load device (ULD) configuration and disruption management in air cargo and multimodal logistics networks. We formulate the ULD loading and placement problem as a Quadratic Unconstrained Binary Optimization (QUBO) model that incorporates weight, volume, center-of-gravity, structural stress, and compatibility constraints. A stratified hybrid architecture spanning physical quantum hardware, algorithmic middleware, and decision-support layers is proposed. Through extensive numerical experiments and comparison with classical solvers, we demonstrate superior payload utilization (up to 96.5%), favorable computational scaling, and rapid disruption recovery. Real-world industry pilots, including quantum-assisted route optimization achieving substantial carbon emission reductions and hybrid solutions at major ports, are analyzed. Results indicate that while noisy intermediate-scale quantum (NISQ) hardware limitations persist, hybrid approaches already deliver measurable gains in operational efficiency, cost reduction, and supply-chain resilience. The work provides a practical roadmap for near-term adoption of quantum technologies in high-stakes logistics environments. Keywords— Quantum annealing, Unit load device (ULD), QUBO, Logistics optimization, Disruption management, Hybrid quantum-classical algorithms, Air cargo, Supply chain resilience
Quantum computing offers transformative potential for addressing complex optimization challenges in logistics and supply chain management.This paper explores the practical applications of quantum annealing and hybrid quantum-classical algorithms within a stratified architecture spanning physical hardware, algorithmic middleware, and decision-support layers.We demonstrate how these technologies enable significant improvements in unit load device (ULD) and pallet configuration, dynamic routing under disruption, and real-time multi-objective optimization.Through analysis of real-world industry pilots, including DHL's quantum-assisted route optimization achieving substantial carbon emission reductions, Volkswagen's fleet efficiency improvements, and hybrid quantum solutions at major ports such as the Port of Los Angeles, we show measurable gains in operational efficiency, cost reduction, payload utilization, and supply chain resilience.The study integrates these advancements with established resilience frameworks and the author's prior contributions in supply chain resilience, automotive battery technologies, and intelligent transportation systems.While hardware limitations in the noisy intermediate-scale quantum (NISQ) era persist, hybrid approaches provide a viable pathway for near-term adoption.This work highlights the transition of quantum technologies from theoretical promise to practical deployment, offering a roadmap for more efficient, resilient, and sustainable global supply chains.Future directions include scaling hybrid systems and expanding applications across multi-modal logistics networks
The major advances in quantum computing over the last few decades have sparked great interest in applying it to solve the most challenging computational problems in a wide variety of areas. One of the most pronounced domains here are optimization problems and a number of algorithmic approaches have been proposed for their solution. For the current noisy intermediate-scale quantum (NISQ) computers the quantum approximate optimization algorithm (QAOA), the variational quantum eigensolver (VQE), and quantum annealing (QA) are the central algorithms for this problem class. The two former can be executed on digital gate-model quantum computers, whereas the latter requires a quantum annealer. Across all hardware architectures and manufactures, the quantum computers available today share the property of being too error-prone to reliably execute involved quantum circuits as they typically arise from quantum optimization algorithms. In order to characterize the limits of existing quantum computers, many component and system level benchmarks have been proposed. However, owing to the complex nature of the errors in quantum systems these benchmark fail to provide predictive power beyond simple quantum circuits and small examples. Application oriented benchmarks have been proposed to remedy this problem, but both, results from real quantum systems as well as use cases beyond constructed academic examples, remain very rare. This paper addresses precisely this gap by considering two industrial relevant use cases: one in the realm of optimizing charging schedules for electric vehicles, the other concerned with the optimization of truck routes. Our central contribution are systematic series of examples derived from these uses cases that we execute on different processors of the gate-based quantum computers of IBM as well as on the quantum annealer of D-Wave.
Quantum computing has emerged as a promising computational paradigm with the potential to solve problems that are intractable for classical computers. This paper provides an overview of the current state of quantum computing, including its historical development, hardware advancements, applications, and challenges. Special attention is given to combinatorial optimization, which is considered one of the most promising application domains. The limitations of current quantum systems and future research directions are also discussed. This paper presents a comparative study between a conventional D-Wave-based simulated annealing approach and a proposed hybrid optimization model for solving largescale combinatorial optimization problems. The study focuses on benchmark problems, including AES, TSP gr24, and a real-world load balancing problem, all formulated as QUBO models. The D-Wave approach employs the dwave-neal simulated annealing engine with various parameter settings, while the proposed model introduces a decomposition-based framework combined with adaptive parameter control and iterative refinement. The experimental results demonstrate that the proposed model consistently outperforms the conventional approach in terms of solution quality, achieving significantly lower energy values across all problem instances. In particular, for the TSP gr24 problem, the proposed model achieves near-optimal solutions with substantially reduced computation time. For large-scale problems such as AES and load balancing, the proposed model maintains scalability and robustness, whereas the conventional approach suffers from increased computational cost and performance degradation. Although the proposed method introduces additional computational steps, its hybrid structure effectively mitigates limitations related to local minima, parameter sensitivity, and scalability. These findings indicate that integrating classical optimization techniques with quantum-inspired methods is essential for practical applications. The proposed model provides a promising framework for solving complex real-world optimization problems and highlights the importance of hybrid approaches in advancing quantum annealing technologies.