Quantum Machine Learning / / ed. by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti, Sourav De.

• Superior document: Title is part of eBook package: De Gruyter DG Ebook Package English 2020
• HerausgeberIn: Behrman, Elizabeth,; Bhattacharyya, Siddhartha,; Chakraborti, Susanta,; De, Sourav,; Mani, Ashish,; Pan, Indrajit,
• MitwirkendeR: Bhattacharyya, Siddhartha,; Bishwas, Arit Kumar,; Chatterjee, Amlan,; De, Sourav,; Dey, Alokananda,; Dey, Sandip,; MacLennan, Bruce J.,; Mani, Ashish,; Palade, Vasile,; Platos, Jan,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2020]; ©2020
• Year of Publication: 2020
• Language: English
• Series: De Gruyter Frontiers in Computational Intelligence , 6
• Physical Description: 1 online resource (XIII, 118 p.)

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.

Table of 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