dc.identifier.uri |
http://dx.doi.org/10.15488/9906 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/9964 |
|
dc.contributor.author |
Beer, Kerstin
|
|
dc.contributor.author |
Bondarenko, Dmytro
|
|
dc.contributor.author |
Farrelly, Terry
|
|
dc.contributor.author |
Osborne, Tobias J.
|
|
dc.contributor.author |
Salzmann, Robert
|
|
dc.contributor.author |
Scheiermann, Daniel
|
|
dc.contributor.author |
Wolf, Ramona
|
|
dc.date.accessioned |
2020-06-29T15:21:49Z |
|
dc.date.available |
2020-06-29T15:21:49Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Beer, K.; Bondarenko, D.; Farrelly, T.; Osborne, T.J.; Salzmann, R. et al.: Training deep quantum neural networks. In: Nature Communications 11 (2020), Nr. 1, 808. DOI: https://doi.org/10.1038/s41467-020-14454-2 |
|
dc.description.abstract |
Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Berlin : Nature Research |
|
dc.relation.ispartofseries |
Nature Communications 11 (2020), Nr. 1 |
|
dc.relation.uri |
https://doi.org/10.1038/s41467-020-14454-2 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
computer simulation |
eng |
dc.subject |
design |
eng |
dc.subject |
learning |
eng |
dc.subject |
optimization |
eng |
dc.subject |
quantum mechanics |
eng |
dc.subject |
research work |
eng |
dc.subject |
training |
eng |
dc.subject |
article |
eng |
dc.subject |
feed forward neural network |
eng |
dc.subject |
learning |
eng |
dc.subject |
memory |
eng |
dc.subject |
nerve cell |
eng |
dc.subject.ddc |
530 | Physik
|
ger |
dc.title |
Training deep quantum neural networks |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.issn |
2041-1723 |
|
dc.relation.doi |
10.1038/s41467-020-14454-2 |
|
dc.bibliographicCitation.issue |
1 |
|
dc.bibliographicCitation.volume |
11 |
|
dc.bibliographicCitation.firstPage |
808 |
|
tib.accessRights |
frei zug�nglich |
|