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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544333/
https://www.ncbi.nlm.nih.gov/pubmed/36206336
http://dx.doi.org/10.1126/sciadv.abn9783
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author 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
author_facet 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
author_sort Zhang, Hui
collection PubMed
description 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.
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spelling pubmed-95443332022-10-24 Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder 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 Sci Adv Physical and Materials Sciences 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. American Association for the Advancement of Science 2022-10-07 /pmc/articles/PMC9544333/ /pubmed/36206336 http://dx.doi.org/10.1126/sciadv.abn9783 Text en Copyright © 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). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Physical and Materials Sciences
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
Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title_full Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title_fullStr Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title_full_unstemmed Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title_short Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
title_sort resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
topic Physical and Materials Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544333/
https://www.ncbi.nlm.nih.gov/pubmed/36206336
http://dx.doi.org/10.1126/sciadv.abn9783
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