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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22758 |
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| _version_ | 1866913765631459328 |
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| author | Han, Siyu Jia, Lihan Guo, Lanzhe |
| author_facet | Han, Siyu Jia, Lihan Guo, Lanzhe |
| contents | This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiple Embeddings for Quantum Machine Learning Han, Siyu Jia, Lihan Guo, Lanzhe Quantum Physics Machine Learning This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications. |
| title | Multiple Embeddings for Quantum Machine Learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2503.22758 |