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Quantum machine learning edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti

Contributor(s): Bhattacharyya, Siddhartha, 1975- | Pan, Indrajit, 1983- | Mani, Ashish [edt, http://id.loc.gov/vocabulary/relators/edt] | De, Sourav, 1979- | Behrman, Elizabeth [edt, http://id.loc.gov/vocabulary/relators/edt] | Chakraborti, Susanta [edt, http://id.loc.gov/vocabulary/relators/edt]Material type: TextTextSeries: De Gruyter frontiers in computational intelligence ; v. 6.Publisher: Berlin Boston De Gruyter [2020]Description: 1 online resourceISBN: 9783110670721; 3110670720; 3110670704; 9783110670707Subject(s): Machine learning | Quantum theory | Algorithmus | Künstliche Intelligenz | Maschinelles Lernen | Quantum Computing | COMPUTERS / Intelligence (AI) & SemanticsGenre/Form: EBSCO eBooks | Electronic books DDC classification: 006.3/1 LOC classification: Q325.5 | .Q36 2020ebOnline resources: EBSCOhost Summary: Quantum-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
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Description based on online resource; title from PDF title page (viewed on September 10, 2020)

Includes bibliographical references and index

Quantum-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

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