QUEST-A: Untrained Filtering with Trained Focusing led to Enhanced Quantum Architectures

• URL: http://arxiv.org/abs/2410.23560v1
• Date: Thu, 31 Oct 2024 01:57:14 GMT
• Title: QUEST-A: Untrained Filtering with Trained Focusing led to Enhanced Quantum Architectures
• 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. This work decomposes QAS into two alternately solved subproblems: optimal circuit structure retrieval and parameter optimization. We propose Quantum Untrained-Explored Synergistic Trained Architecture (QUEST-A), which implements rapid architecture pruning through inherent circuit properties.
• Score: 14.288836269941207
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• Abstract: Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning, with SOTA methods primarily categorized into training-free and gradient-guided approaches. However, treating QAS solely as either a discrete pruning process or a continuous optimization problem fails to balance accuracy and efficiency. This work decomposes QAS into two alternately solved subproblems: optimal circuit structure retrieval and parameter optimization. Based on this insight, we propose Quantum Untrained-Explored Synergistic Trained Architecture (QUEST-A), which implements rapid architecture pruning through inherent circuit properties and develops focused optimization with parameter reuse strategies. QUEST-A unifies discrete structure search and continuous parameter optimization within an evolutionary framework that integrates rapid pruning and fine-grained optimization. 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. These results validate QUEST-A's effectiveness and provide transferable methodologies for QAS.

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