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 performant QML circuits that are also robust to hardware noise, 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 \'Eliv\'agar, a novel resource-efficient, noise-guided QCS framework. \'Eliv\'agar 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. \'Eliv\'agar 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, \'Eliv\'agar 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, \'Eliv\'agar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of \'Eliv\'agar on 12 real quantum devices and 9 QML applications, \'Eliv\'agar achieves 5.3% higher accuracy and a 271$\times$ speedup compared to state-of-the-art QCS methods.

翻译：设计兼具高性能与噪声鲁棒性的量子机器学习电路极具挑战性——其设计空间随电路规模呈指数级增长，且缺乏成熟的量子机器学习电路设计指导原则。虽然近期量子电路搜索方法试图搜寻既具备高性能又对硬件噪声鲁棒的量子机器学习电路，但这些方法直接沿用了经典神经架构搜索的设计范式，未能适配量子硬件的独特约束，导致搜索开销高昂且存在严重性能瓶颈。本文提出Élivágar——一种新型资源高效、噪声引导的量子电路搜索框架。该框架在搜索空间、搜索算法及候选评估策略三大核心环节均实现创新，以解决当前类经典量子电路搜索方法的设计缺陷。通过基于噪声感知与设备拓扑感知的候选生成策略，Élivágar实现了硬件效率并避免了昂贵的电路映射协同搜索。通过引入两个低计算成本的预测器——克利福德噪声鲁棒性与表征容量，Élivágar将噪声鲁棒性评估与性能评估解耦，从而能够提前剔除低保真电路并降低电路评估开销。得益于资源高效特性，Élivágar还可进一步搜索数据嵌入方案，显著提升性能。在12个真实量子设备与9个量子机器学习应用上的综合评估表明，与最先进的量子电路搜索方法相比，Élivágar实现了5.3%的准确率提升与271倍加速比。