Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles
Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/11/1685 |
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| author | Zhaolong Huang Qiting Li Junling Zhao Meimei Song |
| author_facet | Zhaolong Huang Qiting Li Junling Zhao Meimei Song |
| author_sort | Zhaolong Huang |
| collection | DOAJ |
| description | Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model. |
| first_indexed | 2024-03-09T18:21:04Z |
| format | Article |
| id | doaj.art-e48e33a71db048d391417d27f2ca4578 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T18:21:04Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e48e33a71db048d391417d27f2ca45782023-11-24T08:19:11ZengMDPI AGEntropy1099-43002022-11-012411168510.3390/e24111685Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial VehiclesZhaolong Huang0Qiting Li1Junling Zhao2Meimei Song3College of Science, Tianjin University of Technology, Tianjin 300384, ChinaR & D Department, China Academy of Launch Vehicle Technology, Beijing 100076, ChinaCollege of Science, Tianjin University of Technology, Tianjin 300384, ChinaCollege of Science, Tianjin University of Technology, Tianjin 300384, ChinaMission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model.https://www.mdpi.com/1099-4300/24/11/1685collision avoidance of UAVsvariational quantum algorithmsvariational quantum eigensolverquantum approximate optimization algorithmconditional value-at-risk |
| spellingShingle | Zhaolong Huang Qiting Li Junling Zhao Meimei Song Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles Entropy collision avoidance of UAVs variational quantum algorithms variational quantum eigensolver quantum approximate optimization algorithm conditional value-at-risk |
| title | Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles |
| title_full | Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles |
| title_fullStr | Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles |
| title_full_unstemmed | Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles |
| title_short | Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles |
| title_sort | variational quantum algorithm applied to collision avoidance of unmanned aerial vehicles |
| topic | collision avoidance of UAVs variational quantum algorithms variational quantum eigensolver quantum approximate optimization algorithm conditional value-at-risk |
| url | https://www.mdpi.com/1099-4300/24/11/1685 |
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