Overview
- Is recent research on quantum machine learning in Industrial Automation
- Provides an introduction to the fundamentals of quantum machine learning and its applications
- Explores how quantum computing boost industrial automation with the help of generative AI and algorithmic innovation
Part of the book series: Information Systems Engineering and Management (ISEM, volume 65)
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About this book
This book focuses on quantum machine learning that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, uses algorithms run on quantum devices, such as quantum computers, to supplement, expedite, or support the work performed by a classical machine learning program. The devices that perform quantum computations are known as quantum computers. Quantum computers have the potential to revolutionize computation by making certain types of classically intractable problems solvable. A few large companies and small start-ups now have functioning non-error-corrected quantum computers composed of several tens of qubits, and some of these are even accessible to the public through the cloud. Additionally, quantum simulators are making strides in fields varying from molecular energetics to many-body physics. Most known use cases fit into four archetypes: quantum simulation, quantum linear algebra for AI and machine learning, quantum optimization and search, and quantum factorization. Advantages of quantum computing are many and to list a few, first, they’re fast. Ultimately, quantum computers have the potential to provide computational power on a scale that traditional computers cannot ever match. In 2019, for example, Google claimed to carry out a calculation in about 200 seconds that would take a classical supercomputer around 10,000 years. Second, they can solve complex problems. The more complex a problem, the harder it is for even a supercomputer to solve. When a classical computer fails, it’s usually because of a huge degree of complexity and many interacting variables. However, due to the concepts of superposition and entanglement, quantum computers can account for all these variables and complexities to reach a solution. Last but not the least, they can run complex simulations. The speed and complexity that quantum computing can achieve means that, in theory, a quantum computer could simulate many intricate systems.
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Table of contents (18 chapters)
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Front Matter
Editors and Affiliations
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Bibliographic Information
Book Title: Quantum Machine Learning in Industrial Automation
Editors: Anupam Ghosh, Soumi Dutta, Asit Kumar Das, Vinod Kumar Shukla, Fernando Moreira
Series Title: Information Systems Engineering and Management
DOI: https://doi.org/10.1007/978-3-031-99786-0
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Hardcover ISBN: 978-3-031-99785-3Published: 02 September 2025
Softcover ISBN: 978-3-031-99788-4Due: 16 September 2026
eBook ISBN: 978-3-031-99786-0Published: 01 September 2025
Series ISSN: 3004-958X
Series E-ISSN: 3004-9598
Edition Number: 1
Number of Pages: X, 456
Number of Illustrations: 15 b/w illustrations, 67 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Quantum Computing