Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder.
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.
Date Issued
2022-10-07
Date Acceptance
2022-08-23
Citation
Science Advances, 2022, 8 (40), pp.1-11
ISSN
2375-2548
Publisher
American Association for the Advancement of Science
Start Page
1
End Page
11
Journal / Book Title
Science Advances
Volume
8
Issue
40
Copyright Statement
© 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
License URL
Sponsor
Engineering & Physical Science Research Council (E
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/36206336
Grant Number
EP/T001062/1
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2022-10-07