Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder

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 fir...

Full description

Saved in:
Bibliographic Details
Published in:Science advances Vol. 8; no. 40; p. eabn9783
Main Authors: Zhang, Hui, Wan, Lingxiao, Haug, Tobias, Mok, Wai-Keong, Paesani, Stefano, Shi, Yuzhi, Cai, Hong, Chin, Lip Ket, Karim, Muhammad Faeyz, Xiao, Limin, Luo, Xianshu, Gao, Feng, Dong, Bin, Assad, Syed, Kim, M. S., Laing, Anthony, Kwek, Leong Chuan, Liu, Ai Qun
Format: Journal Article
Language:English
Published: American Association for the Advancement of Science 07.10.2022
Subjects:
Optics
Physical and Materials Sciences
Physical Sciences
SciAdv r-articles
ISSN:2375-2548, 2375-2548
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. A quantum autoencoder for teleporting qutrits in a compress-teleport-decompress manner with silicon photonic chips is presented.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
These authors contribute equally to this work.
ISSN:2375-2548
2375-2548
DOI:10.1126/sciadv.abn9783