Efficient Data Loading with Quantum Autoencoder
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISBN
978-1-7281-6327-7
Date Issued
2023-01-01
Author(s)
Abstract
Faithfully 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.
Subjects
autoencoder | data loading | quantum machine learning | variational quantum circuit
Type
conference paper