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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.12773 |
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| _version_ | 1866916528749805568 |
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| author | Gili, Marta Sebastian, Paul San Blázquez-García, Ane |
| author_facet | Gili, Marta Sebastian, Paul San Blázquez-García, Ane |
| contents | The Tail Assignment Problem (TAP) is a critical optimization challenge in airline operations, requiring the optimal assignment of aircraft to scheduled flights to maximize efficiency and minimize costs. To address the TAP, this work applies the Quantum Approximate Optimization Algorithm (QAOA), a promising quantum computing algorithm developed for tackling complex combinatorial optimization problems. A detailed formulation of the TAP is provided and QAOA's performance is evaluated on realistic problem instances, examining its strengths and weaknesses. Additionally, QAOA is compared with classical methods such as brute force and branch-and-price, as well as Quantum Annealing (QA), another quantum approach. The analysis reveals the current limitations of quantum hardware but suggests potential advantages as technology advances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization of Flight Routes: Quantum Approximate Optimization Algorithm for the Tail Assignment Problem Gili, Marta Sebastian, Paul San Blázquez-García, Ane Quantum Physics The Tail Assignment Problem (TAP) is a critical optimization challenge in airline operations, requiring the optimal assignment of aircraft to scheduled flights to maximize efficiency and minimize costs. To address the TAP, this work applies the Quantum Approximate Optimization Algorithm (QAOA), a promising quantum computing algorithm developed for tackling complex combinatorial optimization problems. A detailed formulation of the TAP is provided and QAOA's performance is evaluated on realistic problem instances, examining its strengths and weaknesses. Additionally, QAOA is compared with classical methods such as brute force and branch-and-price, as well as Quantum Annealing (QA), another quantum approach. The analysis reveals the current limitations of quantum hardware but suggests potential advantages as technology advances. |
| title | Optimization of Flight Routes: Quantum Approximate Optimization Algorithm for the Tail Assignment Problem |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2412.12773 |