Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Algorithm in Path Optimization for Autonomous Underwater Vehicle: Hardware-in-the-Loop experiment

Viet-Dung Do, Xuan-Kien Dang, Žarko Koboević, Tien-Dat Tran, Chi-Luan Le

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Autonomous Underwater Vehicles (AUVs) are often used in survey, exploration, and monitoring missions of the maritime environment. Due to operating in complex environments, planning an optimal path to avoid obstacles is crucial and directly affects operational efficiency and safety. This study proposes a Quantum Algorithm (QA) for planning an AUV optimal path. The QA is based on determining the optimal path by computing the quantum nodes in a Three-Dimensional (3D) space with obstacles. The proposed algorithm computes optimal waypoints, which are assessed by safety requirements established at the stage. The results have a fast-processing time and are effective under complex operating conditions. The feasible results, compared with other pathfinding algorithms, have demonstrated the effectiveness of the proposed solution. In addition, this study develops experiments using a Hardware-in-the-Loop (HIL) model to evaluate the efficiency of the proposed solution under operating conditions that closely resemble actual conditions.

DOI
10.17818/nm/2026/3.1

Related papers

Quantum optimization in autonomous underwater vehicle routing navigation

Quantum optimization is currently developing strongly in many research fields. This method uses the principle of quantum mechanics to determine the process of quantum fluctuations in optimal calculations. In this study, we use the Quantum Annealing Algorithm (QAA) to optimize the journey of the Autonomous Underwater Vehicle (AUV) to find the shortest distance, consume the least energy, and even avoid obstacles while moving. The research results on simulations are compared with the traditional Dijkstra method to evaluate the effectiveness of the proposed method, thereby affirming the superiority of quantum optimization in the automatic control of AUV.

Journal of Transportation Science and Technology•Mar 1, 2025•0 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 Technology•Mar 31, 2026•1 citation

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance

Nouhaila Innan, Muhammad Kashif, Alberto Marchisio +3 more

The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first applications of QAOA for drone trajectory optimization. QUAV models pathfinding as a quantum optimization problem, allowing efficient exploration of multiple paths while incorporating obstacle constraints and geospatial accuracy through UTM coordinate transformation. A theoretical analysis shows that QUAV achieves linear scaling in circuit depth relative to the number of edges, under fixed optimization settings. Extensive simulations and a realhardware implementation on IBM's ibm_kyiv backend validate its performance and robustness under noise. Despite hardware constraints, results demonstrate that QUAV generates feasible, efficient trajectories, highlighting the promise of quantum approaches for future drone navigation systems.

arXiv:2508.21361•IEEE International Conference on Quantum Artificial Intelligence (QAI)•Nov 2, 2025•3 citations

Quantum Control and Fuzzy Logic-based Model for Autonomous Vehicles and Submarine Exploration Technologies

Preeti Pandey, Preeti Chahal

Quantum Control and Fuzzy Logic can be integrated to provide a significant improvement in the performance and decision-making of autonomous system in dynamic environment. Quantum control takes advantage of optimization tasks solvable by quantum systems and fuzzy logic manages the uncertainty and inaccuracy of real time decision making. The paper will also explain how these technologies can be utilized together in autonomous vehicles and in the exploration of the ocean and how the combination can make it more efficient and flexible and trustworthy. This paper explores advantages, difficulties and uses of quantum control using fuzzy logic. The paper examines the main principles of both technologies, case studies of their successful implementation, and performance measures. The issues such as technical complexity, scalability, and integration with classical systems are also discussed. Findings indicate significant improvements in vehicles and submarines. With autonomous cars, the system improved route planning and traffic control, minimizing travel time and fuel consumption. It was found to be of great use in underwater exploration in submarines, improving obstacle avoidance and navigation in complex underwater conditions up to$20-30 \%$in efficiency. Fuzzy logic and quantum control together hold great promise for the advancement of autonomous technologies in the future, even in the face of scalability and error correction issues. The full potential of a hybrid system and quantum error correction can be realized through further research.

International Conference on Intelligent Computing and Control Systems (ICICCS)•Mar 16, 2026•0 citations

A Quantum Computing Approach for Multi-robot Coverage Path Planning

Poojith U Rao, Florian Speelman, Balwinder Sodhi +1 more

This paper tackles the multi-vehicle Coverage Path Planning (CPP) problem, crucial for applications like search and rescue or environmental monitoring. Due to its NP-hard nature, finding optimal solutions becomes infeasible with larger problem sizes. This motivates the development of heuristic approaches that enhance efficiency even marginally. We propose a novel approach for exploring paths in a 2D grid, specifically designed for easy integration with the Quantum Alternating Operator Ansatz (QAOA), a powerful quantum heuristic. Our contribution includes: 1) An objective function tailored to solve the multi-vehicle CPP using QAOA. 2) Theoretical proofs guaranteeing the validity of the proposed approach. 3) Efficient construction of QAOA operators for practical implementation. 4) Resource estimation to assess the feasibility of QAOA execution. 5) Performance comparison against established algorithms like the Depth First Search. This work paves the way for leveraging quantum computing in optimizing multi-vehicle path planning, potentially leading to real-world advancements in various applications.

arXiv:2407.08767•Jul 11, 2024•0 citations

Path Planning for Autonomous Robots using Quantum Reinforcement Learning

L. Tharun, .Swetha Shailaja, G. Aswitha +1 more

ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT•Feb 26, 2026•0 citations
Autonomous Underwater Vehicles (AUVs) are often used in survey, exploration, and monitoring missions of the maritime environment. Due to operating in complex environments, planning an optimal path to avoid obstacles is crucial and directly affects operational efficiency and safety. This study proposes a Quantum Algorithm (QA) for planning an AUV optimal path. The QA is based on determining the optimal path by computing the quantum nodes in a Three-Dimensional (3D) space with obstacles. The proposed algorithm computes optimal waypoints, which are assessed by safety requirements established at the stage. The results have a fast-processing time and are effective under complex operating conditions. The feasible results, compared with other pathfinding algorithms, have demonstrated the effectiveness of the proposed solution. In addition, this study develops experiments using a Hardware-in-the-Loop (HIL) model to evaluate the efficiency of the proposed solution under operating conditions that closely resemble actual conditions.
Quantum optimization is currently developing strongly in many research fields. This method uses the principle of quantum mechanics to determine the process of quantum fluctuations in optimal calculations. In this study, we use the Quantum Annealing Algorithm (QAA) to optimize the journey of the Autonomous Underwater Vehicle (AUV) to find the shortest distance, consume the least energy, and even avoid obstacles while moving. The research results on simulations are compared with the traditional Dijkstra method to evaluate the effectiveness of the proposed method, thereby affirming the superiority of quantum optimization in the automatic control of AUV.
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.
The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first applications of QAOA for drone trajectory optimization. QUAV models pathfinding as a quantum optimization problem, allowing efficient exploration of multiple paths while incorporating obstacle constraints and geospatial accuracy through UTM coordinate transformation. A theoretical analysis shows that QUAV achieves linear scaling in circuit depth relative to the number of edges, under fixed optimization settings. Extensive simulations and a realhardware implementation on IBM's ibm_kyiv backend validate its performance and robustness under noise. Despite hardware constraints, results demonstrate that QUAV generates feasible, efficient trajectories, highlighting the promise of quantum approaches for future drone navigation systems.
Quantum Control and Fuzzy Logic can be integrated to provide a significant improvement in the performance and decision-making of autonomous system in dynamic environment. Quantum control takes advantage of optimization tasks solvable by quantum systems and fuzzy logic manages the uncertainty and inaccuracy of real time decision making. The paper will also explain how these technologies can be utilized together in autonomous vehicles and in the exploration of the ocean and how the combination can make it more efficient and flexible and trustworthy. This paper explores advantages, difficulties and uses of quantum control using fuzzy logic. The paper examines the main principles of both technologies, case studies of their successful implementation, and performance measures. The issues such as technical complexity, scalability, and integration with classical systems are also discussed. Findings indicate significant improvements in vehicles and submarines. With autonomous cars, the system improved route planning and traffic control, minimizing travel time and fuel consumption. It was found to be of great use in underwater exploration in submarines, improving obstacle avoidance and navigation in complex underwater conditions up to20−30%20-30 \%20−30%in efficiency. Fuzzy logic and quantum control together hold great promise for the advancement of autonomous technologies in the future, even in the face of scalability and error correction issues. The full potential of a hybrid system and quantum error correction can be realized through further research.
This paper tackles the multi-vehicle Coverage Path Planning (CPP) problem, crucial for applications like search and rescue or environmental monitoring. Due to its NP-hard nature, finding optimal solutions becomes infeasible with larger problem sizes. This motivates the development of heuristic approaches that enhance efficiency even marginally. We propose a novel approach for exploring paths in a 2D grid, specifically designed for easy integration with the Quantum Alternating Operator Ansatz (QAOA), a powerful quantum heuristic. Our contribution includes: 1) An objective function tailored to solve the multi-vehicle CPP using QAOA. 2) Theoretical proofs guaranteeing the validity of the proposed approach. 3) Efficient construction of QAOA operators for practical implementation. 4) Resource estimation to assess the feasibility of QAOA execution. 5) Performance comparison against established algorithms like the Depth First Search. This work paves the way for leveraging quantum computing in optimizing multi-vehicle path planning, potentially leading to real-world advancements in various applications.
ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.