TY  - JOUR
AU  - Locher, David
AU  - Cardarelli, Lorenzo
AU  - Müller, Markus
TI  - Quantum Error Correction with Quantum Autoencoders
JO  - Quantum
VL  - 7
SN  - 2521-327X
CY  - Wien
PB  - Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
M1  - FZJ-2023-01404
SP  - 942 -
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000979644900001
DO  - DOI:10.22331/q-2023-03-09-942
UR  - https://juser.fz-juelich.de/record/1005289
ER  -