Utilizing unsupervised representation learning for quantum architecture search (QAS) represents a cutting-edge approach poised to realize potential quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices. Most QAS algorithms combine their search space and search algorithms together and thus generally require evaluating a large number of quantum circuits during the search process. Predictor-based QAS algorithms can alleviate this problem by directly estimating the performance of circuits according to their structures. However, a high-performance predictor generally requires very time-consuming labeling to obtain a large number of labeled quantum circuits. Recently, a classical neural architecture search algorithm Arch2vec inspires us by showing that architecture search can benefit from decoupling unsupervised representation learning from the search process. Whether unsupervised representation learning can help QAS without any predictor is still an open topic. In this work, we propose a framework QAS with unsupervised representation learning and visualize how unsupervised architecture representation learning encourages quantum circuit architectures with similar connections and operators to cluster together. Specifically, our framework enables the process of QAS to be decoupled from unsupervised architecture representation learning so that the learned representation can be directly applied to different downstream applications. Furthermore, our framework is predictor-free eliminating the need for a large number of labeled quantum circuits. During the search process, we use two algorithms REINFORCE and Bayesian Optimization to directly search on the latent representation, and compare them with the method Random Search. The results show our framework can more efficiently get well-performing candidate circuits within a limited number of searches.

翻译：利用无监督表示学习进行量子架构搜索（QAS）代表了一种前沿方法，有望在含噪中等规模量子（NISQ）设备上实现潜在量子优势。大多数QAS算法将搜索空间与搜索算法结合，因此在搜索过程中通常需要评估大量量子电路。基于预测器的QAS算法可通过直接根据电路结构估算其性能来缓解该问题。然而，高性能预测器通常需要极其耗时的标注过程以获取大量带标签的量子电路。近期，经典神经架构搜索算法Arch2vec通过证明架构搜索可从解耦无监督表示学习与搜索过程获益而给我们带来启发。无监督表示学习能否在不依赖任何预测器的情况下帮助QAS仍是一个开放性问题。本工作中，我们提出一个结合无监督表示学习的QAS框架，并可视化揭示了无监督架构表示学习如何促使具有相似连接和算符的量子电路结构聚类。具体而言，我们的框架使QAS过程与无监督架构表示学习解耦，使学习到的表示可直接应用于不同的下游任务。此外，该框架无需预测器，从而消除了对大量带标签量子电路的需求。搜索过程中，我们采用REINFORCE算法和贝叶斯优化两种方法直接在潜在表示上进行搜索，并将其与随机搜索方法进行比较。结果表明，我们的框架能在有限搜索次数内更高效地获得性能优异的候选电路。