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Training of quantum circuits on a hybrid quantum computer

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint 11 Oct 2019Embargo end date: 01 Jan 2018 English Publisher:American Association for the Advancement of Science (AAAS)Journal:Science Advances, volume 5 (eissn: 2375-2548, Copyright policy )Funded by:NSF | Convergence QL: Workshop ..., NSF | Joint Quantum Institute: ...
Authors: D. Zhu; N. M. Linke; M. Benedetti; K. A. Landsman; N. H. Nguyen; C. H. Alderete; A. Perdomo-Ortiz; +6 Authors

doi: 10.1126/sciadv.aaw9918 , 10.48550/arxiv.1812.08862

pmid: 31667342

pmc: PMC6799983

arXiv: 1812.08862

Training of quantum circuits on a hybrid quantum computer

Abstract

We train generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off.

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Keywords

Quantum Physics, FOS: Physical sciences, Quantum Physics (quant-ph), Research Articles

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 167
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
167
Top 1%
Top 10%
Top 1%
Green
gold
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