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Quantum computing and artificial intelligence : training machine and deep learning algorithms on quantum computers / edited by Pethuru Raj, Abhishek Kumar, Ashutosh Kumar Dubey, Surbhi Bhatia and Oswalt Manoj S.
- Format:
- Book
- Series:
- Quantum computing series
- Quantum Computing Series
- Language:
- English
- Physical Description:
- 1 online resource (308 pages)
- Edition:
- 1st ed.
- Place of Publication:
- Berlin ; Boston : Walter de Gruyter GmbH, [2023].
- Summary:
- This book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.
- Contents:
- Intro
- Preface
- Contents
- List of contributing authors
- Chapter 1 Digital transformation technology and tools: shaping the future of primary health care
- Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms
- Chapter 3 A deep survey on quantum computing technologies
- Chapter 4 Machine learning and deep learning
- Chapter 5 From evolution to revolution: the contemporary development of quantum computing
- Chapter 6 Real-time big data analytics
- Chapter 7 Quantum processors/networks/sensors
- Chapter 8 Quantum computing in automata theory
- Chapter 9 Quantum computing: future of artificial intelligence and its applications
- Chapter 10 A leap among quantum ML and DL models: a review
- Chapter 11 A perspective study on quantum machine learning models for the areas of medicine, materials, sensing, and communication
- Chapter 12 Quantum computing: application-specific need of the hour
- Chapter 13 Industrial Internet of things and Industry 4.0: a learner's perspectives toward quantum technologies
- Chapter 14 Applications of quantum AI for healthcare
- Biography
- Index.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9783110791402
- 3110791404
- OCLC:
- 1393308822
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