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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2312.10242 |
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| _version_ | 1866916133997641728 |
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| author | Chen, I-Chi Singh, Harshdeep Anukruti, V L Quanz, Brian Yogaraj, Kavitha |
| author_facet | Chen, I-Chi Singh, Harshdeep Anukruti, V L Quanz, Brian Yogaraj, Kavitha |
| contents | Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models. We re-implement a key representative set of these existing methods, adapting an image classification approach using quantum self-attention to create a quantum hybrid transformer that works for text and image classification, and applying quantum self-attention and quantum recurrent neural networks to natural language processing tasks. We also explore different encoding techniques and introduce positional encoding into quantum self-attention neural networks leading to improved accuracy and faster convergence in text and image classification experiments. This paper also performs a comparative analysis of classical self-attention models and their quantum counterparts, helping shed light on the differences in these models and their performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10242 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey of Classical And Quantum Sequence Models Chen, I-Chi Singh, Harshdeep Anukruti, V L Quanz, Brian Yogaraj, Kavitha Quantum Physics Machine Learning Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models. We re-implement a key representative set of these existing methods, adapting an image classification approach using quantum self-attention to create a quantum hybrid transformer that works for text and image classification, and applying quantum self-attention and quantum recurrent neural networks to natural language processing tasks. We also explore different encoding techniques and introduce positional encoding into quantum self-attention neural networks leading to improved accuracy and faster convergence in text and image classification experiments. This paper also performs a comparative analysis of classical self-attention models and their quantum counterparts, helping shed light on the differences in these models and their performance. |
| title | A Survey of Classical And Quantum Sequence Models |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2312.10242 |