Investigating methods to solve large windfarm optimization problems with a minimum number of qubits using circuit-based quantum computers
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908722639405056 |
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| author | Hancock, James Craven, Matthew McNeile, Craig |
| author_facet | Hancock, James Craven, Matthew McNeile, Craig |
| contents | This study investigates quantum computing approaches for solving the windfarm layout optimization (WFLO) problems formulated as a quadratic unconstrained binary optimization (QUBO) problem. We investigate two encoding methods that require fewer than one qubit per grid point: the previously developed Pauli correlation encoding (PCE) and a novel single-qubit operator encoding (SQOE). These methods are tested on three windfarm configurations - two from prior WFLO scaling studies and a new real-world model based on an existing windfarm in Wales. The improved encoding methods allow us to solve WFLO problems on $9\times 9$ grids using up to 20 qubits on a quantum computer simulator. The results show that both encoding methods perform competitively and demonstrate favorable scaling characteristics across the tested systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating methods to solve large windfarm optimization problems with a minimum number of qubits using circuit-based quantum computers Hancock, James Craven, Matthew McNeile, Craig Quantum Physics 68Q12, 90C27 F.1.2; G.1.6 This study investigates quantum computing approaches for solving the windfarm layout optimization (WFLO) problems formulated as a quadratic unconstrained binary optimization (QUBO) problem. We investigate two encoding methods that require fewer than one qubit per grid point: the previously developed Pauli correlation encoding (PCE) and a novel single-qubit operator encoding (SQOE). These methods are tested on three windfarm configurations - two from prior WFLO scaling studies and a new real-world model based on an existing windfarm in Wales. The improved encoding methods allow us to solve WFLO problems on $9\times 9$ grids using up to 20 qubits on a quantum computer simulator. The results show that both encoding methods perform competitively and demonstrate favorable scaling characteristics across the tested systems. |
| title | Investigating methods to solve large windfarm optimization problems with a minimum number of qubits using circuit-based quantum computers |
| topic | Quantum Physics 68Q12, 90C27 F.1.2; G.1.6 |
| url | https://arxiv.org/abs/2512.17582 |