# Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search Source: [Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:54:50.780Z Authors: Lian-Hui Yu, Xiao-Yu Li, Geng Chen, Qin-Sheng Zhu, Hui Li, Guo-Wu Yang Published: 2024-10-31T01:57:14.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()