Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search

• URL: http://arxiv.org/abs/2410.23560v2
• Date: Mon, 11 Nov 2024 07:50:31 GMT
• Title: Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search
• Authors: Lian-Hui Yu, Xiao-Yu Li, Geng Chen, Qin-Sheng Zhu, Hui Li, Guo-Wu Yang,
• Abstract summary: Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. We decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. QUEST-A develops an evolutionary mechanism with knowledge accumulation and reuse to enhance multi-level knowledge transfer.
• Score: 14.288836269941207
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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