Quantum Computing Approaches for Mission Covering Optimization
Quantum computing has the potential to revolutionize the way hard computational problems are solved in terms of speed and accuracy. Quantum hardware is an active area of research and different hardware platforms are being developed. Quantum algorithms target each hardware implementation and bring ad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/7/224 |
| _version_ | 1827625015944675328 |
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| author | Massimiliano Cutugno Annarita Giani Paul M. Alsing Laura Wessing Austar Schnore |
| author_facet | Massimiliano Cutugno Annarita Giani Paul M. Alsing Laura Wessing Austar Schnore |
| author_sort | Massimiliano Cutugno |
| collection | DOAJ |
| description | Quantum computing has the potential to revolutionize the way hard computational problems are solved in terms of speed and accuracy. Quantum hardware is an active area of research and different hardware platforms are being developed. Quantum algorithms target each hardware implementation and bring advantages to specific applications. The focus of this paper is to compare how well quantum annealing techniques and the QAOA models constrained optimization problems. As a use case, a constrained optimization problem called mission covering optimization is used. Quantum annealing is implemented in adiabatic hardware such as D-Wave, and QAOA is implemented in gate-based hardware such as IBM. This effort provides results in terms of cost, timing, constraints held, and qubits used. |
| first_indexed | 2024-03-09T12:21:40Z |
| format | Article |
| id | doaj.art-448639da2cef41dcb204ae797a423920 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T12:21:40Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-448639da2cef41dcb204ae797a4239202023-11-30T22:39:36ZengMDPI AGAlgorithms1999-48932022-06-0115722410.3390/a15070224Quantum Computing Approaches for Mission Covering OptimizationMassimiliano Cutugno0Annarita Giani1Paul M. Alsing2Laura Wessing3Austar Schnore4Air Force Research Lab, Information Directorate, Rome, NY 13441, USAGE Research, General Electric, Niskayuna, NY 12309, USAAir Force Research Lab, Information Directorate, Rome, NY 13441, USAAir Force Research Lab, Information Directorate, Rome, NY 13441, USAGE Research, General Electric, Niskayuna, NY 12309, USAQuantum computing has the potential to revolutionize the way hard computational problems are solved in terms of speed and accuracy. Quantum hardware is an active area of research and different hardware platforms are being developed. Quantum algorithms target each hardware implementation and bring advantages to specific applications. The focus of this paper is to compare how well quantum annealing techniques and the QAOA models constrained optimization problems. As a use case, a constrained optimization problem called mission covering optimization is used. Quantum annealing is implemented in adiabatic hardware such as D-Wave, and QAOA is implemented in gate-based hardware such as IBM. This effort provides results in terms of cost, timing, constraints held, and qubits used.https://www.mdpi.com/1999-4893/15/7/224quantum computingquantum annealingNISQ devicesconstrained optimizationconstraint satisfaction problems |
| spellingShingle | Massimiliano Cutugno Annarita Giani Paul M. Alsing Laura Wessing Austar Schnore Quantum Computing Approaches for Mission Covering Optimization Algorithms quantum computing quantum annealing NISQ devices constrained optimization constraint satisfaction problems |
| title | Quantum Computing Approaches for Mission Covering Optimization |
| title_full | Quantum Computing Approaches for Mission Covering Optimization |
| title_fullStr | Quantum Computing Approaches for Mission Covering Optimization |
| title_full_unstemmed | Quantum Computing Approaches for Mission Covering Optimization |
| title_short | Quantum Computing Approaches for Mission Covering Optimization |
| title_sort | quantum computing approaches for mission covering optimization |
| topic | quantum computing quantum annealing NISQ devices constrained optimization constraint satisfaction problems |
| url | https://www.mdpi.com/1999-4893/15/7/224 |
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