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.
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 annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods. However, due to the limited number of qubits and their connectivity, the QA hardware did not show such an advantage over classical methods in past benchmarking studies. Recent advancements in QA with more than 5000 qubits, enhanced qubit connectivity, and the hybrid architecture promise to realize the quantum advantage. Here, we use a quantum annealer with state-of-the-art techniques and benchmark its performance against classical solvers. To compare their performance, we solve over 50 optimization problem instances represented by large and dense Hamiltonian matrices using quantum and classical solvers. The results demonstrate that a state-of-the-art quantum solver has higher accuracy (~0.013%) and a significantly faster problem-solving time (~6561×) than the best classical solver. Our results highlight the advantages of leveraging QA over classical counterparts, particularly in hybrid configurations, for achieving high accuracy and substantially reduced problem solving time in large-scale real-world optimization problems.
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.
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.
Airline companies struggle every day trying to schedule crew, flights and aircraft.Tail Assignment is the problem of allocating individual aircraft to a set of flights, while ensuring multiple constraints and aiming to minimise an objective function, such as operational costs.Given the enormous amount of possibilities and constraints involved, this problem has been a study case for the last decade.Many solutions have emerged using classical computing, but with limitations regarding performance.Quantum Annealing (QA) is a heuristic technique for finding global minimum energy levels over an energy landscape using quantum mechanics.Due to its characteristics, it has been proved to have a clear advantage in solving some complex optimisation problems, being a promising technique to apply in multiple fields.In this study, Tail Assignment Problem was set as a Quadratic Unconstrained Binary Optimisation (QUBO) model, using two different techniques, and was solved using a classical and two hybrid solvers.Tests were run based on extractions from real-world data, analysing the performance of the implementation in terms of time, scalability and quality (i.e., the lowest operational costs) of the obtained solutions.We concluded that using a library for modelling the problem as well as considering individual flights rather than pre-aggregating them in strings can be a bottleneck when it comes to its scalability.Furthermore, we found out that there was a higher probability of obtaining better solutions for this problem using one of the hybrid solvers when compared with the classical heuristic algorithms such as Simulated Annealing (SA).These findings can serve as foundations for further studies.
Quantum Annealing (QA) is a heuristic search algorithm that can run on adiabatic quantum computation processors to solve combinatorial optimization problems. Although theoretical studies and simulations on classical hardware have shown encouraging results, these analyses often assume that the computation occurs in adiabatically closed systems without environmental interference. This is not a realistic assumption for real systems; therefore, without extensive empirical measurements on real quantum platforms, theory-based predictions, simulations on classical hardware, or limited tests do not accurately assess the current commercial capabilities. This study has assessed the quality of the solution provided by a commercial quantum annealing platform compared to known solutions for the capacitated vehicle routing problem. This study has conducted an extensive analysis on more than 30 hours of access to QA commercial platforms to investigate how the size of the problem and its complexity impact the accuracy of the solution and the time used to find a solution. Our results have found that the absolute error is between 0.12 and 0.55, and the time of the quantum processor unit is between 30 and 46 μs. Our results show that as the constraint density increases, the quality of the solution degrades. Therefore, more than the size of the problem, the complexity of the model plays a critical role, and practical applications should select formulations that minimize the constraint density.
Quantum annealing, a maturing technology, offers a potential solution to large scale combinatorial optimization problems by leveraging quantum bits (qubits) in superposition to explore vast solution spaces simultaneously, significantly reducing computation time.Currently, quantum annealers can handle up to 11,000 binary variables, with hybrid solvers capable of addressing problems with up to 1 million binary variables.Though not yet consistently optimal, these approaches provide high-quality solutions without the need for complex heuristics.This paper explores the potential application of quantum annealing to optimize supply chain operations at Dell, specifically within their Excess & Obsolete (E&O) inventory process.The focus is on identifying a proactive re-balancing strategy for inventory management, minimizing costs, and improving parts distribution across Dell's global locations.By utilizing simulated annealing accelerated by high-performance computing (HPC) resources, including Nvidia GPUs and NEC's vector engine, we aim to achieve near-optimal solutions for inventory rebalancing.Some models will also be evaluated on quantum hardware to explore the feasibility of quantum acceleration in real-world supply chain optimization.Preliminary results suggest that while quantum annealing is still in its early stages, it holds significant promise for cost savings and operational efficiency in largescale supply chains.