  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.
