Quantum Embedding with Transformer for High-dimensional Data
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929248681328640 |
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| author | Chen, Hao-Yuan Chang, Yen-Jui Liao, Shih-Wei Chang, Ching-Ray |
| author_facet | Chen, Hao-Yuan Chang, Yen-Jui Liao, Shih-Wei Chang, Ching-Ray |
| contents | Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12704 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Embedding with Transformer for High-dimensional Data Chen, Hao-Yuan Chang, Yen-Jui Liao, Shih-Wei Chang, Ching-Ray Quantum Physics Machine Learning Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems. |
| title | Quantum Embedding with Transformer for High-dimensional Data |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2402.12704 |