Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915053015400448 |
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| author | Dimitrijevs, Maksims Kālis, Mārtiņš Repko, Iļja |
| author_facet | Dimitrijevs, Maksims Kālis, Mārtiņš Repko, Iļja |
| contents | Time-series forecasting is essential for strategic planning and resource allocation. In this work, we explore two quantum-based approaches for time-series forecasting. The first approach utilizes a Parameterized Quantum Circuit (PQC) model. The second approach employs Variational Quantum Linear Regression (VQLS), enabling time-series forecasting by encoding the problem as a system of linear equations, which is then solved using quantum optimization techniques. We compare the results of these two methods to evaluate their effectiveness and potential advantages for practical forecasting applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting Dimitrijevs, Maksims Kālis, Mārtiņš Repko, Iļja Quantum Physics Time-series forecasting is essential for strategic planning and resource allocation. In this work, we explore two quantum-based approaches for time-series forecasting. The first approach utilizes a Parameterized Quantum Circuit (PQC) model. The second approach employs Variational Quantum Linear Regression (VQLS), enabling time-series forecasting by encoding the problem as a system of linear equations, which is then solved using quantum optimization techniques. We compare the results of these two methods to evaluate their effectiveness and potential advantages for practical forecasting applications. |
| title | Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2412.05615 |