Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.

变分量子算法 (VQA) 有望成为在嘈杂的中等规模量子设备上获得量子优势的途径。然而，经验和理论结果都表明，部署的 ansatz 严重影响 VQAs 的性能，因此具有更多量子门的 ansatz 可以实现更强的表达能力，而累积的噪声可能会导致可训练性差。为了最大限度地提高 VQA 的鲁棒性和可训练性，我们设计了一种资源和运行时高效的方案，称为量子架构搜索 (QAS)。特别是，给定一个学习任务，QAS 会自动寻找一个接近最优的 ansatz（即电路架构）来平衡通过添加更多噪声量子门带来的好处和副作用，以实现良好的性能。我们通过 IBM 云在数值模拟器和真实量子硬件上实施 QAS，以完成数据分类和量子化学任务。在所研究的问题中，数值和实验结果表明，QAS 不仅可以减轻量子噪声和贫瘠高原的影响，而且优于具有预选 ansatze 的 VQAs。