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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.06892 |
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| _version_ | 1866913786226540544 |
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| author | Maragkopoulos, G. Stefanakos, N. Mandilara, A. Syvridis, D. |
| author_facet | Maragkopoulos, G. Stefanakos, N. Mandilara, A. Syvridis, D. |
| contents | We combine classical and quantum Machine Learning (ML) techniques to effectively analyze long time-series data acquired during experiments. Specifically, we demonstrate that replacing a deep classical neural network with a thoughtfully designed Variational Quantum Circuit (VQC) in an ML pipeline for multiclass classification of time-series data yields the same classification performance, while significantly reducing the number of trainable parameters. To achieve this, we use a VQC based on a single qudit, and encode the classical data into the VQC via a trainable hybrid autoencoder which has been recently proposed as embedding technique. Our results highlight the importance of tailored data pre-processing for the circuit and show the potential of qudit-based VQCs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06892 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study Maragkopoulos, G. Stefanakos, N. Mandilara, A. Syvridis, D. Quantum Physics We combine classical and quantum Machine Learning (ML) techniques to effectively analyze long time-series data acquired during experiments. Specifically, we demonstrate that replacing a deep classical neural network with a thoughtfully designed Variational Quantum Circuit (VQC) in an ML pipeline for multiclass classification of time-series data yields the same classification performance, while significantly reducing the number of trainable parameters. To achieve this, we use a VQC based on a single qudit, and encode the classical data into the VQC via a trainable hybrid autoencoder which has been recently proposed as embedding technique. Our results highlight the importance of tailored data pre-processing for the circuit and show the potential of qudit-based VQCs. |
| title | Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.06892 |