Wu, Siang RueiSiang RueiWuLi, Chun TseChun TseLiHAO-CHUNG CHENG2024-01-182024-01-182023-01-01978-1-7281-6327-715206149https://scholars.lib.ntu.edu.tw/handle/123456789/638629Faithfully loading classical data into a quantum system is a core problem in quantum machine learning and various quantum information processing tasks. In this work, we propose an efficient quantum autoencoder architecture that can construct a quantum state approximating the unknown classical distribution with high precision and with only linear circuit depth. Simulation experiments show that our proposed method substantially outperforms state-of-the-art methods on a wide range of datasets by evaluating divergences between the loaded distributions and the target distribution, and it also enjoys a faster convergence rate and stability. Moreover, the proposed scheme can be efficiently implemented on near-term hybrid classical-quantum systems with very shallow circuit depths.autoencoder | data loading | quantum machine learning | variational quantum circuitconference paper10.1109/ICASSP49357.2023.100964962-s2.0-85180404350https://api.elsevier.com/content/abstract/scopus_id/85180404350