Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
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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.
I: Science Advances, Bind 8, Nr. 40, 9783, 07.10.2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder
AU - Zhang, Hui
AU - Wan, Lingxiao
AU - Haug, Tobias
AU - Mok, Wai-Keong
AU - Paesani, Stefano
AU - Shi, Yuzhi
AU - Cai, Hong
AU - Chin, Lip Ket
AU - Karim, Muhammad Faeyz
AU - Xiao, Limin
AU - Luo, Xianshu
AU - Gao, Feng
AU - Dong, Bin
AU - Assad, Syed
AU - Kim, M. S.
AU - Laing, Anthony
AU - Kwek, Leong Chuan
AU - Liu, Ai Qun
PY - 2022/10/7
Y1 - 2022/10/7
N2 - 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. Unsu-pervised 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), obtain-ing 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.
AB - 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. Unsu-pervised 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), obtain-ing 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.
KW - NEURAL-NETWORKS
KW - ENTANGLEMENT
KW - GENERATION
KW - STATES
U2 - 10.1126/sciadv.abn9783
DO - 10.1126/sciadv.abn9783
M3 - Journal article
C2 - 36206336
VL - 8
JO - Science advances
JF - Science advances
SN - 2375-2548
IS - 40
M1 - 9783
ER -
ID: 327937361