International Conference on System, Computation, Automation and Networking (ICSCAN)

Optimizing Flight Routes for Better Fuel Efficiency and Optimal Path using Grover's Algorithm & QAOA Algorithm - Quantum Computing

Muhammad Imran Khan, I Govindharaj, R. Sathishkumar, D H Jenifer, T. Maeghasree, K. Yohini

Published Dec 27, 2024• 2 citations

Abstract

Optimization of flight routes is one of the hard problems in the aviation sector. This paper focuses on the prospects of quantum computing developed using Grover's Algorithm and the Quantum Approximate Optimization Algorithm (QAOA). Grover's Algorithm provides a quadratic speedup for searching an unsorted database and is therefore quite useful for identifying optimum flight routes amongst millions of choices. Preliminary benchmarking suggests that it can efficiently search a space of more than one million routes in far less time than the conventional approach, which is important for timely decisions over aviation. Simultaneously, QAOA refines candidate routes with the aid of real-time inputs such as weather and air traffic, and each time delivers near-optimal solutions even during sudden changes. The quantum framework in development would make it possible to improve the accuracy of routes recommended and respond fast to changing circumstances. This includes weather conditions whereby, based on research, estimated savings in fuel by optimizing routes run between 5% to 10%, which translates to costs lower with fewer carbon emissions playing a role in environmental sustainability. Flight scheduling also improves through quantum algorithms because of increased operational efficiency, which would enable airlines to cope better with complicated constraints and not allow delays when flights are involved. In summary, the Grover's Algorithm and QAOA hybrid shows a more holistic approach of giving an advantage regarding speed over the traditional approach as well as flexibility.

DOI: 10.1109/icscan62807.2024.10894075

Related papers

Quantum Optimization for Airline Itinerary and Scheduling: A QUBO and QAOA Approach

Vaibhav V. Khedekar

Abstract Airline itinerary and scheduling represent some of the most difficult computational problems that exist in transportation. The problems require choosing the best flight routes while determining schedules and allocating resources through various constraints which include regulatory requirements and maintenance schedules and dynamic demand patterns. The solution of small and medium-scale instances has been achieved through mixed-integer programming and heuristic methods including simulated annealing and genetic algorithms and branch-and-price but these methods become impractical for larger real-world instances due to exponential scaling issues. Quantum computing has introduced fresh solutions which enable the resolution of these problems. Quantum mechanical phenomena particularly superposition and entanglement enable optimization algorithms to examine an exponentially expanded solution space simultaneously. Our research applies a basic airline scheduling problem to the Traveling Salesman Problem (TSP) before converting it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The Quantum Approximate Optimization Algorithm (QAOA) serves as a hybrid quantum-classical method to solve this formulation. The experimental evaluation of a 4-city scenario shows that quantum-based optimization generates performance levels comparable to traditional methods and indicates that improved quantum hardware development will provide computational benefits for complex airline scheduling problems. This research holds dual importance because it can minimize operational expenses and improve scheduling performance and it establishes fundamental principles for complex combinatorial problem optimization across the transportation industry. The research establishes fundamental principles for quantum-enabled applications that will revolutionize airline operations and other transportation networks by solving critical scalability and solution quality problems.

Apr 22, 20250 citations

Potential Energy Savings from Quantum Computing-Based Route Optimization

Ayush Nadiger, Adriana Caraeni, Katie Schouten

We investigate the potential of the Quantum Approximate Optimization Algorithm (QAOA) for reducing energy consumption in route planning, a key challenge in logistics due to the NP-hard nature of the Traveling Salesman and Vehicle Routing Problems. By encoding route optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implementing QAOA circuits at depth p = 3-5 alongside classical baselines of Simulated Annealing (SA) and Genetic Algorithms (GA), we perform systematic benchmarks on Euclidean graphs of sizes N = 5, 10, and 20. Our results demonstrate that QAOA attains higher solution quality with approximation ratios of 0.953 (N = 5), 0.921 (N = 10), and 0.903 (N = 20), outperforming SA and GA by 2.7-4.4%. Wall-clock runtimes for QAOA are 2-3x faster than SA across all tested sizes, and energy consumption measurements reveal a three-order-of-magnitude reduction, remaining in the picojoule range versus nanojoules for classical methods. Translating these gains to real-world logistics suggests an 8.2% improvement in routing efficiency could save approximately 2.62 EJ of fuel annually in the U.S., avoiding nearly 1.94 x 10^8 tonnes of CO2 emissions. These findings highlight QAOA's promise as a fast, energy-efficient optimizer for sustainable logistics applications and underscore its potential role in next-generation fleet-management systems.

arXiv:2604.16718Apr 17, 20260 citations

HYBRID QUANTUM ALGORITHMS FOR AUTONOMOUS VEHICLE NAVIGATION: DIRECTED PATH PLANNING WITH GROVER-QAOA

IDRIS KAGAN DEMIR, Seda Nur Gungor, Mehmet Karaköse

Path planning in complex and obstacle-constrained environments remains a challenging combinatorial optimization problem due to the exponential growth of the solution space and the limitations of classical algorithms in dynamic decision-making scenarios. Traditional approaches such as Dijkstra or A* may experience high computational costs and susceptibility to local optima when dealing with large-scale directed graphs containing obstacles. This study proposes a hybrid quantum computing approach that integrates the Grover Search Algorithm and the Quantum Approximate Optimization Algorithm (QAOA) to address optimal path planning on obstacle-oriented directed graphs. The Grover algorithm is first employed as an oracle-based filtering mechanism to detect and eliminate obstacle-containing edges, thereby generating a valid subgraph of feasible paths. Following this preprocessing step, QAOA is applied to the filtered graph within a nonlinear optimization framework to determine the minimum-cost path between source and destination nodes. The optimization model represents the graph as G=(V,E), where binary decision variables indicate whether an edge is included in the selected route while satisfying flow conservation constraints. By narrowing the solution space through Grover-based obstacle detection and performing optimization only on valid edges, the proposed method significantly reduces computational complexity compared to conventional approaches. Experimental analysis demonstrates that the hybrid quantum model effectively identifies optimal routes and adapts to different obstacle configurations while maintaining efficient convergence behavior. The results highlight the potential of integrating quantum search and quantum optimization techniques for solving combinatorial graph problems. This work contributes to the growing body of research on quantum-assisted optimization and demonstrates the feasibility of applying hybrid quantum algorithms to directed path planning problems relevant to autonomous navigation systems.

Quantum Journal of Engineering Science and TechnologyMar 31, 20261 citation

Quantum-Accelerated Flight Selection: Probing Grover's Algorithm and Quantum Device Efficiency

Jayesh V. Hire, Vaidehi Gawande, Sagar Dhande

Current flight search platforms primarily consider four essential factors when planning a trip: departure/arrival dates, as well as the origin and destination locations. However, when additional parameters are added to this search, the problem shifts from a simple to a complex search, as the engine must sift through a massive dataset of flights, including information on airlines, flight routes, fees, and more. To address this challenge and improve flight search efficiency amidst data-intensive and resource-demanding environments, this paper proposes the use of Grover's search algorithm. This algorithm is demonstrated as the optimal solution for searches with increased constraints. The paper highlights the practical application of Grover's search algorithm across three datasets of assorted sizes, as well as various quantum hardware and simulators available in the NISQ era. Furthermore, this paper provides an in-depth understanding of the complexity of quantum circuit design, including the key phases of state encoding and amplitude amplification. The effectiveness of these approaches is evaluated through analysis of execution times and quantum measurement results. The aim is to showcase the potential of quantum computing in revolutionizing real-world search tasks, particularly in the realm of flight selection.

International Journal of Innovative Science and Research Technology (IJISRT)Aug 31, 20241 citation

Quantum Computing Applications for Flight Trajectory Optimization

Henry Makhanov, Kanav Setia, Junyu Liu +2 more

Major players in the global aerospace industry are shifting their focus toward achieving net carbon-neutral operations by 2050. A considerable portion of the overall carbon emission reduction is expected to come from new aircraft technologies, such as flight path optimization. In pursuing these sustainability objectives, we delve into the capacity of quantum computing to tackle computational challenges associated with flight path optimization, an essential operation within the aerospace engineering domain with important ecological and economic considerations. In recent years, the quantum computing field has made significant strides, paving the way for improved performance over classical algorithms. In order to effectively apply quantum algorithms in real-world scenarios, it is crucial to thoroughly examine and tackle the intrinsic overheads and constraints that exist in the present implementations of these algorithms. Our study delves into the application of quantum computers in flight path optimization problems and introduces a customizable modular framework designed to accommodate specific simulation requirements. We examine the running time of a hybrid quantum-classical algorithm across various quantum architectures and their simulations on CPUs and GPUs. A temporal comparison between the conventional classical algorithm and its quantum-improved counterpart indicates that achieving the theoretical speedup in practice may necessitate further innovation. We present our results from running the quantum algorithms on IBM hardware and discuss potential approaches to accelerate the incorporation of quantum algorithms within the problem domain.

arXiv:2304.14445International Conference on Quantum Communications, Networking, and Computing (QCNC)Jul 1, 20249 citations

Exploring the use of quantum algorithms for logistics optimization

Arnav Sonavane, Amit Aylani

The logistics industry faces increasing complexities that demand real-time adaptability and efficient decision-making. Traditional computing methods struggle with massive data volumes and intricate optimization problems, but quantum computing offers revolutionary potential. This research explores the transformative possibilities of quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA), for optimizing logistics operations. We investigate whether quantum computing can revolutionize logistics and supply chain management by addressing inherent complexities. The study delves into the application of quantum algorithms in areas such as route optimization, demand forecasting, and disruption management. Our comprehensive literature review unveils the potential of QAOA in surpassing classical computing for determining efficient shipping routes, considering factors like weather, geopolitical concerns, and disruptions.

Chapman & Hall/CRC eBooksJun 11, 20257 citations