Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

A quantum autoencoder: using machine learning to compress qutrits

Not Accessible

Your library or personal account may give you access

Abstract

The compression of quantum data will allow increased control over difficult-to- manage quantum resources. We experimentally realize a quantum autoencoder, which learns to compress quantum data with a classical machine learning routine.

© 2020 The Author(s)

PDF Article
More Like This
Machine Learning Assisted Quantum Photonics

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
QM6B.3 Quantum 2.0 (QUANTUM) 2020

Merging Machine Learning with Quantum Photonics: Rapid classification of quantum sources

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
FM4C.4 CLEO: QELS_Fundamental Science (CLEO:FS) 2020

On-Chip Quantum Autoencoder for Teleportation of High-Dimensional Quantum States

H. Zhang, L. Wan, T. Haug, WK Mok, M. S. Kim, L. C. Kwek, and A. Q. Liu
FW1A.3 CLEO: QELS_Fundamental Science (CLEO:FS) 2022

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.