Narayan Vyas, an academician at Vivekananda Global University, specializes in computer science, focusing on IoT and Mobile App Development. He has trained over 1000 students globally and published extensively in Scopus journals. An active IEEE member, his research interests include Remote Sensing, Machine Learning, and Computer Vision.
Quantum Machine Learning: Quantum Algorithms and Neural Networks
Price: 159,95 € | Publisher: De Gruyter
Editors: Pethuru Raj, Houbing Herbert Song, Dac-Nhuong Le, Narayan Vyas
Release Date: 6 August 2024 | Copyright: © 2024 | Language: English | Categories: Technology, AI
ISBN: 978-3111342092 | DOI: 10.1515/9783111342276
Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.
Provides an overview of the basic concepts, preliminaries, and principles of quantum computing and machine learning. Presents the most advanced and well-known quantum machine learning and optimization algorithms.
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Chapter 2: An exploration of quantum computing: concept, architecture, and innovative applications
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Chapter 3: Quantum machine learning in healthcare: diagnostics and drug discovery
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Chapter 4: Quantum machine learning in finance
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Chapter 5: Crucial role of blockchain in quantum computing: enhancing security and trust
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Chapter 6: Algorithmic exploration of unveiling fault tolerance in quantum machine learning
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Chapter 7: Quantum machine learning in renewable energy systems
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Chapter 8: Decentralized quantum machine learning: distributed quantum computing for enhanced learning
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Chapter 9: Quantum reinforcement learning: decision-making in quantum environments
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Chapter 10: Quantum machine learning in natural language processing: opportunities and challenges
Authors: Umesh Kumar Lilhore, Sarita Simaiya
Chapter 11: Unveiling intelligence: exploring variational quantum circuits as machine learning models
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Chapter 12: Methods and tools to improve quantum software quality: a survey
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Chapter 13: Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning
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Chapter 14: Future trends and research horizons in quantum machine learning
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