講演情報
[15p-K406-7]Quantum Architecture Search with Neural Predictor Based on ZX-Diagram
〇Shanchuan Li1, Daisuke Tsukayama1, Jun-ichi Shirakashi1, Tetsuo Shibuya2, Hiroshi Imai2 (1.Tokyo Univ. Agr. & Tech., 2.Univ. Tokyo)
キーワード：
Variational Quantum Eigensolver、Gate-Based Quantum Computer、Quantum Architecture Search
Quantum architecture search (QAS) has attracted significant attention as a strategy to automate the design of parameterized quantum circuits in variational quantum algorithms (VQAs). Yet, it often requires evaluating a large number of candidate circuits, leading to high computational costs. Performance predictors help mitigate this issue by quickly estimating circuit “quality,” thereby reducing the number of circuits that must undergo resource-intensive, high-fidelity optimizations. In the noisy intermediate-scale quantum (NISQ) era, VQAs are widely adopted thanks to their error resilience and flexible demands on quantum hardware, though their performance depends strongly on the structure of the underlying quantum circuits. QAS, which systematically explores circuit configurations, naturally complements VQAs by automating the search for high-performance circuit designs.