- eBook:Quantum Machine Learning
- Author:Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Elizabeth Behrman, Susanta Chakraborti
- Edition:1 edition
- Data:June 8, 2020
- Pages:236 pages
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. For example, the outcome of the measurement of a qubit could reveal the result of a binary classification task. 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.
The salient features of the book include:
- In depth analysis of the subject matter with mathematical discourse
- Video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents.
- Examples on real life applications.
- Illustrative diagrams
- Coding examples
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
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