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dc.contributor.authorBhattacharyya, Siddhartha
dc.contributor.authorPan, Indrajit
dc.contributor.authorMani, Ashish
dc.contributor.authorDe, Sourav
dc.contributor.authorBehrman, Elizabeth C.
dc.contributor.authorChakrabarti, Susanta
dc.date.accessioned2020-10-20T20:27:31Z
dc.date.available2020-10-20T20:27:31Z
dc.date.issued2020-06-08
dc.identifier.citationBhattacharyya, S., Pan, I., Mani, A., De, S., Behrman, E., & Chakraborti, S. (Eds.). (2020). Quantum Machine Learning. Berlin, Boston: De Gruyteren_US
dc.identifier.isbn978-311067070-7
dc.identifier.urihttps://doi.org/10.1515/9783110670707
dc.identifier.urihttps://soar.wichita.edu/handle/10057/19541
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractQuantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. New trends in Machine Learning based on Quantum Computing and Quantum Algorithms Examples on real life applications Illustrative diagrams and coding examples.en_US
dc.language.isoen_USen_US
dc.publisherDe Gruyteren_US
dc.relation.ispartofseriesQuantum Machine Learning;2020
dc.subjectInteratomic potentialen_US
dc.subjectPotential energy surfacesen_US
dc.subjectMaterials scienceen_US
dc.titleQuantum machine learningen_US
dc.typeBooken_US
dc.rights.holder© 2020 Walter de Gruyter GmbHen_US


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