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@ARTICLE{Locher:1005289,
author = {Locher, David and Cardarelli, Lorenzo and Müller, Markus},
title = {{Q}uantum {E}rror {C}orrection with {Q}uantum
{A}utoencoders},
journal = {Quantum},
volume = {7},
issn = {2521-327X},
address = {Wien},
publisher = {Verein zur Förderung des Open Access Publizierens in den
Quantenwissenschaften},
reportid = {FZJ-2023-01404},
pages = {942 -},
year = {2023},
abstract = {Active quantum error correction is a central ingredient to
achieve robust quantum processors. Inthis paper we
investigate the potential of quantum machine learning for
quantum error correction.Specifically, we demonstrate how
quantum neural networks, in the form of quantum
autoencoders,can be trained to learn optimal strategies for
active detection and correction of errors,
includingspatially correlated computational errors as well
as qubit losses. We highlight that the denoisingcapabilities
of quantum autoencoders are not limited to the protection of
specific states but extendto the entire logical codespace.
We also show that quantum neural networks can be used to
discovernew logical encodings that are optimally adapted to
the underlying noise. Moreover, we find that,even in the
presence of moderate noise in the quantum autoencoders
themselves, they may still besuccessfully used to perform
beneficial quantum error correction.},
cin = {PGI-2},
ddc = {530},
cid = {I:(DE-Juel1)PGI-2-20110106},
pnm = {5221 - Advanced Solid-State Qubits and Qubit Systems
(POF4-522)},
pid = {G:(DE-HGF)POF4-5221},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000979644900001},
doi = {10.22331/q-2023-03-09-942},
url = {https://juser.fz-juelich.de/record/1005289},
}