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Article . 2017 . Peer-reviewed
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Article . 2017
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https://dx.doi.org/10.1126/sci...
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Machine learning for quantum physics

descriptionPublicationkeyboard_double_arrow_right Article 10 Feb 2017 English Publisher:American Association for the Advancement of Science (AAAS)Journal:Science, volume 355, pages 580-580 (issn: 0036-8075, eissn: 1095-9203, Copyright policy )
Authors: Michael R, Hush;

doi: 10.1126/science.aam6564

pmid: 28183936

Machine learning for quantum physics

Abstract

An artificial neural network can discover the ground state of a quantum many-body system

Related Organizations
Keywords

Machine Learning, Physics, Quantum Theory

  • BIP!
    Impact byBIP!
    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).
    		 28
    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 10%
    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.
    		 Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
28
Top 10%
Top 10%
Top 10%
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