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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.23560 |
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| _version_ | 1866915011813703680 |
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| author | Yu, Lian-Hui Li, Xiao-Yu Chen, Geng Zhu, Qin-Sheng Li, Hui Yang, Guo-Wu |
| author_facet | Yu, Lian-Hui Li, Xiao-Yu Chen, Geng Zhu, Qin-Sheng Li, Hui Yang, Guo-Wu |
| contents | Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. Unlike previous methods that treat it as a static search process, from a perspective on QAS as an item retrieval task in vast search space, we decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. We propose quantum untrained-explored synergistic trained architecture (QUEST-A),a framework through coarse-grained untrained filtering for rapid search space reduction and fine-grained trained focusing for precise space refinement in progressive QAS. QUEST-A develops an evolutionary mechanism with knowledge accumulation and reuse to enhance multi-level knowledge transfer in architecture searching. Experiments demonstrate QUEST-A's superiority over existing methods: enhancing model expressivity in signal representation, maintaining high performance across varying complexities in image classification, and achieving order-of-magnitude precision improvements in variational quantum eigensolver tasks, providing a transferable methodology for QAS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search Yu, Lian-Hui Li, Xiao-Yu Chen, Geng Zhu, Qin-Sheng Li, Hui Yang, Guo-Wu Quantum Physics Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. Unlike previous methods that treat it as a static search process, from a perspective on QAS as an item retrieval task in vast search space, we decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. We propose quantum untrained-explored synergistic trained architecture (QUEST-A),a framework through coarse-grained untrained filtering for rapid search space reduction and fine-grained trained focusing for precise space refinement in progressive QAS. QUEST-A develops an evolutionary mechanism with knowledge accumulation and reuse to enhance multi-level knowledge transfer in architecture searching. Experiments demonstrate QUEST-A's superiority over existing methods: enhancing model expressivity in signal representation, maintaining high performance across varying complexities in image classification, and achieving order-of-magnitude precision improvements in variational quantum eigensolver tasks, providing a transferable methodology for QAS. |
| title | Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2410.23560 |