Quantum Machine Learning / / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De.
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 inf...
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| Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2020] ©2020 |
| Year of Publication: | 2020 |
| Language: | English |
| Series: | De Gruyter Frontiers in Computational Intelligence ,
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| Physical Description: | 1 online resource (XIII, 118 p.) |
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Quantum Machine Learning / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De. Berlin ; Boston : De Gruyter, [2020] ©2020 1 online resource (XIII, 118 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda De Gruyter Frontiers in Computational Intelligence , 2512-8868 ; 6 Frontmatter -- Contents -- List of Contributors -- Preface -- 1. Introduction to quantum machine learning -- 2. Topographic representation for quantum machine learning -- 3. Quantum optimization for machine learning -- 4. From classical to quantum machine learning -- 5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm -- 6. Conclusion -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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. Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023) Algorithmus. Künstliche Intelligenz. Maschinelles Lernen. Quantum Computing. COMPUTERS / Intelligence (AI) & Semantics. bisacsh Behrman, Elizabeth, editor. edt http://id.loc.gov/vocabulary/relators/edt Bhattacharyya, Siddhartha, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bhattacharyya, Siddhartha, editor. edt http://id.loc.gov/vocabulary/relators/edt Bishwas, Arit Kumar, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Chakraborti, Susanta, editor. edt http://id.loc.gov/vocabulary/relators/edt Chatterjee, Amlan, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb De, Sourav, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb De, Sourav, editor. edt http://id.loc.gov/vocabulary/relators/edt Dey, Alokananda, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Dey, Sandip, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb MacLennan, Bruce J., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mani, Ashish, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mani, Ashish, editor. edt http://id.loc.gov/vocabulary/relators/edt Palade, Vasile, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Pan, Indrajit, editor. edt http://id.loc.gov/vocabulary/relators/edt Platos, Jan, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Title is part of eBook package: De Gruyter DG Ebook Package English 2020 9783110696288 Title is part of eBook package: De Gruyter DG Plus DeG Package 2020 Part 1 9783110696271 Title is part of eBook package: De Gruyter De Gruyter English eBooks 2020 - UC 9783110659061 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2020 English 9783110704716 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2020 9783110704518 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2020 English 9783110704815 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2020 9783110704617 ZDB-23-DEI EPUB 9783110670721 print 9783110670646 https://doi.org/10.1515/9783110670707 https://www.degruyter.com/isbn/9783110670707 Cover https://www.degruyter.com/document/cover/isbn/9783110670707/original |
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Behrman, Elizabeth, Behrman, Elizabeth, Bhattacharyya, Siddhartha, Bhattacharyya, Siddhartha, Bhattacharyya, Siddhartha, Bhattacharyya, Siddhartha, Bishwas, Arit Kumar, Bishwas, Arit Kumar, Chakraborti, Susanta, Chakraborti, Susanta, Chatterjee, Amlan, Chatterjee, Amlan, De, Sourav, De, Sourav, De, Sourav, De, Sourav, Dey, Alokananda, Dey, Alokananda, Dey, Sandip, Dey, Sandip, MacLennan, Bruce J., MacLennan, Bruce J., Mani, Ashish, Mani, Ashish, Mani, Ashish, Mani, Ashish, Palade, Vasile, Palade, Vasile, Pan, Indrajit, Pan, Indrajit, Platos, Jan, Platos, Jan, |
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Quantum Machine Learning / |
| spellingShingle |
Quantum Machine Learning / De Gruyter Frontiers in Computational Intelligence , Frontmatter -- Contents -- List of Contributors -- Preface -- 1. Introduction to quantum machine learning -- 2. Topographic representation for quantum machine learning -- 3. Quantum optimization for machine learning -- 4. From classical to quantum machine learning -- 5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm -- 6. Conclusion -- Index |
| title_full |
Quantum Machine Learning / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De. |
| title_fullStr |
Quantum Machine Learning / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De. |
| title_full_unstemmed |
Quantum Machine Learning / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De. |
| title_auth |
Quantum Machine Learning / |
| title_alt |
Frontmatter -- Contents -- List of Contributors -- Preface -- 1. Introduction to quantum machine learning -- 2. Topographic representation for quantum machine learning -- 3. Quantum optimization for machine learning -- 4. From classical to quantum machine learning -- 5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm -- 6. Conclusion -- Index |
| title_new |
Quantum Machine Learning / |
| title_sort |
quantum machine learning / |
| series |
De Gruyter Frontiers in Computational Intelligence , |
| series2 |
De Gruyter Frontiers in Computational Intelligence , |
| publisher |
De Gruyter, |
| publishDate |
2020 |
| physical |
1 online resource (XIII, 118 p.) Issued also in print. |
| contents |
Frontmatter -- Contents -- List of Contributors -- Preface -- 1. Introduction to quantum machine learning -- 2. Topographic representation for quantum machine learning -- 3. Quantum optimization for machine learning -- 4. From classical to quantum machine learning -- 5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm -- 6. Conclusion -- Index |
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9783110670707 9783110696288 9783110696271 9783110659061 9783110704716 9783110704518 9783110704815 9783110704617 9783110670721 9783110670646 |
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2512-8868 ; |
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https://doi.org/10.1515/9783110670707 https://www.degruyter.com/isbn/9783110670707 https://www.degruyter.com/document/cover/isbn/9783110670707/original |
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Not Illustrated |
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Quantum Machine Learning / |
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