Quantum Machine Learning
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Edited by:
Siddhartha Bhattacharyya
, Indrajit Pan , Ashish Mani , Sourav De , Elizabeth Behrman and Susanta Chakraborti
About this book
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.
- New trends in Machine Learning based on Quantum Computing and Quantum Algorithms
- Examples on real life applications
- Illustrative diagrams and coding examples
Author / Editor information
Topics
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Frontmatter
I -
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Contents
VII -
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List of Contributors
IX -
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Preface
XI -
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1. Introduction to quantum machine learning
1 -
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2. Topographic representation for quantum machine learning
11 -
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3. Quantum optimization for machine learning
39 -
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4. From classical to quantum machine learning
67 -
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5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm
89 -
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6. Conclusion
115 -
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Index
117
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