Multiobjective Optimization and Network Routing With Near-Term Quantum Computers
Multiobjective optimization is a ubiquitous problem that arises naturally in many scientific and industrial areas. Network routing optimization with multiobjective performance demands falls into this problem class, and finding good quality solutions at large scales is generally challenging. In this...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10502334/ |
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| _version_ | 1832586881109327872 |
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| author | Shao-Hen Chiew Kilian Poirier Rajesh Mishra Ulrike Bornheimer Ewan Munro Si Han Foon Christopher Wanru Chen Wei Sheng Lim Chee Wei Nga |
| author_facet | Shao-Hen Chiew Kilian Poirier Rajesh Mishra Ulrike Bornheimer Ewan Munro Si Han Foon Christopher Wanru Chen Wei Sheng Lim Chee Wei Nga |
| author_sort | Shao-Hen Chiew |
| collection | DOAJ |
| description | Multiobjective optimization is a ubiquitous problem that arises naturally in many scientific and industrial areas. Network routing optimization with multiobjective performance demands falls into this problem class, and finding good quality solutions at large scales is generally challenging. In this work, we develop a scheme with which near-term quantum computers can be applied to solve multiobjective combinatorial optimization problems. We study the application of this scheme to the network routing problem in detail, by first mapping it to the multiobjective shortest-path problem. Focusing on an implementation based on the quantum approximate optimization algorithm (QAOA)—the go-to approach for tackling optimization problems on near-term quantum computers—we examine the Pareto plot that results from the scheme and qualitatively analyze its ability to produce Pareto-optimal solutions. We further provide theoretical and numerical scaling analyses of the resource requirements and performance of QAOA and identify key challenges associated with this approach. Finally, through Amazon Braket, we execute small-scale implementations of our scheme on the IonQ Harmony 11-qubit quantum computer. |
| format | Article |
| id | doaj-art-ac2c5a347a784832883342a7c3216ac2 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-ac2c5a347a784832883342a7c3216ac22025-01-25T00:03:31ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511910.1109/TQE.2024.338675310502334Multiobjective Optimization and Network Routing With Near-Term Quantum ComputersShao-Hen Chiew0https://orcid.org/0000-0003-3398-087XKilian Poirier1https://orcid.org/0009-0000-5105-8083Rajesh Mishra2https://orcid.org/0009-0005-4410-1940Ulrike Bornheimer3https://orcid.org/0009-0005-0100-0912Ewan Munro4https://orcid.org/0000-0002-8928-4032Si Han Foon5Christopher Wanru Chen6Wei Sheng Lim7https://orcid.org/0009-0008-8777-0396Chee Wei Nga8Entropica Labs, SingaporeEntropica Labs, SingaporeEntropica Labs, SingaporeEntropica Labs, SingaporeEntropica Labs, SingaporeDefence Science and Technology Agency, SingaporeDefence Science and Technology Agency, SingaporeDefence Science and Technology Agency, SingaporeDefence Science and Technology Agency, SingaporeMultiobjective optimization is a ubiquitous problem that arises naturally in many scientific and industrial areas. Network routing optimization with multiobjective performance demands falls into this problem class, and finding good quality solutions at large scales is generally challenging. In this work, we develop a scheme with which near-term quantum computers can be applied to solve multiobjective combinatorial optimization problems. We study the application of this scheme to the network routing problem in detail, by first mapping it to the multiobjective shortest-path problem. Focusing on an implementation based on the quantum approximate optimization algorithm (QAOA)—the go-to approach for tackling optimization problems on near-term quantum computers—we examine the Pareto plot that results from the scheme and qualitatively analyze its ability to produce Pareto-optimal solutions. We further provide theoretical and numerical scaling analyses of the resource requirements and performance of QAOA and identify key challenges associated with this approach. Finally, through Amazon Braket, we execute small-scale implementations of our scheme on the IonQ Harmony 11-qubit quantum computer.https://ieeexplore.ieee.org/document/10502334/Approximation algorithmshardwarenetworksoptimizationquantum circuitquantum computing |
| spellingShingle | Shao-Hen Chiew Kilian Poirier Rajesh Mishra Ulrike Bornheimer Ewan Munro Si Han Foon Christopher Wanru Chen Wei Sheng Lim Chee Wei Nga Multiobjective Optimization and Network Routing With Near-Term Quantum Computers IEEE Transactions on Quantum Engineering Approximation algorithms hardware networks optimization quantum circuit quantum computing |
| title | Multiobjective Optimization and Network Routing With Near-Term Quantum Computers |
| title_full | Multiobjective Optimization and Network Routing With Near-Term Quantum Computers |
| title_fullStr | Multiobjective Optimization and Network Routing With Near-Term Quantum Computers |
| title_full_unstemmed | Multiobjective Optimization and Network Routing With Near-Term Quantum Computers |
| title_short | Multiobjective Optimization and Network Routing With Near-Term Quantum Computers |
| title_sort | multiobjective optimization and network routing with near term quantum computers |
| topic | Approximation algorithms hardware networks optimization quantum circuit quantum computing |
| url | https://ieeexplore.ieee.org/document/10502334/ |
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