一句话：提出噪声引导的资源高效量子电路搜索框架 Élivágar，通过拓扑感知生成与廉价预测器加速 QML 分类电路设计。
量子机器学习（QML）电路设计空间随规模指数增长且缺乏指导原则；现有量子电路搜索（QCS）直接沿用经典神经架构搜索，与量子硬件约束不匹配，造成高搜索开销与性能瓶颈。本文提出 Élivágar，一个资源高效、噪声引导的 QCS 框架，在搜索空间、搜索算法与候选评估三方面创新：基于噪声和设备拓扑生成候选以避免电路映射联合搜索；引入 Clifford 噪声鲁棒性与表示容量两个廉价预测器，解耦噪声鲁棒性和性能评估，提前剔除低保真电路并降低评估成本；还可搜索数据嵌入。作者在 12 台真实量子设备和 9 个 QML 应用上评估，相比最先进 QCS 方法准确率提高 5.3%，搜索速度提升 271 倍。
Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging --- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for such circuits, they directly adopt designs from classical Neural Architecture Search (NAS) that are misaligned with the unique constraints of quantum hardware, resulting in high search overheads and severe performance bottlenecks. We present Élivágar, a novel resource-efficient, noise-guided QCS framework. Élivágar innovates in all three major aspects of QCS --- search space, search algorithm and candidate evaluation strategy --- to address the design flaws in current classically-inspired QCS methods. Élivágar achieves hardware-efficiency and avoids an expensive circuit-mapping co-search via noise- and device topology-aware candidate generation. By introducing two cheap-to-compute predictors, Clifford noise resilience and representational capacity, Élivágar decouples the evaluation of noise robustness and performance, enabling early rejection of low-fidelity circuits and reducing circuit evaluation costs. Due to its resource-efficiency, Élivágar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of Élivágar on 12 real quantum devices and 9 QML applications, Élivágar achieves 5.3% higher accuracy and a 271× speedup compared to state-of-the-art QCS methods.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{AnagolumA0QS24,
title = {Elivagar: Efficient Quantum Circuit Search for Classification},
author = {Sashwat Anagolum and Narges Alavisamani and Poulami Das and Moinuddin Qureshi and Yunong Shi},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640354}
}