Quantum Architecture Search with Unsupervised Representation Learning

• URL: http://arxiv.org/abs/2401.11576v2
• Date: Tue, 19 Mar 2024 12:53:24 GMT
• Title: Quantum Architecture Search with Unsupervised Representation Learning
• Authors: Yize Sun, Zixin Wu, Yunpu Ma, Volker Tresp,
• Abstract summary: We propose a framework for unsupervised representation learning for quantum architecture search (QAS) Our framework is predictor-free eliminating the need for a large number of labeled quantum circuits. The results show our framework can more efficiently get well-performing candidate circuits within a limited number of searches.
• Score: 24.698519892763283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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