Quantum Architecture Search with Unsupervised Representation Learning

arXiv:2401.11576v2 Announce Type: replace-cross
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 …

abstract algorithms architecture arxiv circuits cs.lg devices edge intermediate nisq quant-ph quantum quantum advantage representation representation learning scale search search algorithms space together type unsupervised