QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917755104526336 |
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| author | Aaraba, Abdallah Cherkaoui, Soumaya Ahmad, Ola Laprade, Jean-Frédéric Nahman-Lévesque, Olivier Vieloszynski, Alexis Wang, Shengrui |
| author_facet | Aaraba, Abdallah Cherkaoui, Soumaya Ahmad, Ola Laprade, Jean-Frédéric Nahman-Lévesque, Olivier Vieloszynski, Alexis Wang, Shengrui |
| contents | Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gaussian process regression stands out as a Bayesian machine learning technique adept at addressing this multifaceted challenge. This paper introduces a novel approach that blends the robustness of this Bayesian technique with the nuanced insights provided by the kernel perspective on quantum models, aimed at advancing quantum kernelized probabilistic forecasting. We incorporate a quantum feature map inspired by Ising interactions and demonstrate its effectiveness in capturing the temporal dependencies critical for precise forecasting. The optimization of our model's hyperparameters circumvents the need for computationally intensive gradient descent by employing gradient-free Bayesian optimization. Comparative benchmarks against established classical kernel models are provided, affirming that our quantum-enhanced approach achieves competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting Aaraba, Abdallah Cherkaoui, Soumaya Ahmad, Ola Laprade, Jean-Frédéric Nahman-Lévesque, Olivier Vieloszynski, Alexis Wang, Shengrui Machine Learning Artificial Intelligence Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gaussian process regression stands out as a Bayesian machine learning technique adept at addressing this multifaceted challenge. This paper introduces a novel approach that blends the robustness of this Bayesian technique with the nuanced insights provided by the kernel perspective on quantum models, aimed at advancing quantum kernelized probabilistic forecasting. We incorporate a quantum feature map inspired by Ising interactions and demonstrate its effectiveness in capturing the temporal dependencies critical for precise forecasting. The optimization of our model's hyperparameters circumvents the need for computationally intensive gradient descent by employing gradient-free Bayesian optimization. Comparative benchmarks against established classical kernel models are provided, affirming that our quantum-enhanced approach achieves competitive performance. |
| title | QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.12007 |