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| 001 | 1005289 | ||
| 005 | 20231027114356.0 | ||
| 024 | 7 | _ | |a 10.22331/q-2023-03-09-942 |2 doi |
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| 100 | 1 | _ | |a Locher, David |0 P:(DE-Juel1)190763 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Quantum Error Correction with Quantum Autoencoders |
| 260 | _ | _ | |a Wien |c 2023 |b Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Cardarelli, Lorenzo |0 P:(DE-Juel1)184904 |b 1 |
| 700 | 1 | _ | |a Müller, Markus |0 P:(DE-Juel1)179396 |b 2 |e Corresponding author |
| 773 | _ | _ | |a 10.22331/q-2023-03-09-942 |g Vol. 7, p. 942 - |0 PERI:(DE-600)2931392-2 |p 942 - |t Quantum |v 7 |y 2023 |x 2521-327X |
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