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Quantum machine learning in industrial automation

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Quantum machine learning in industrial automation/ edited by Anupam Ghosh ... [et al.].
其他作者:	Ghosh, Anupam.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	x, 456 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Quantum computing. -
電子資源:	https://doi.org/10.1007/978-3-031-99786-0
ISBN:	9783031997860

Quantum machine learning in industrial automation
Quantum machine learning in industrial automation[electronic resource] /edited by Anupam Ghosh ... [et al.]. - Cham :Springer Nature Switzerland :2025. - x, 456 p. :ill. (some col.), digital ;24 cm. - Information systems engineering and management,v. 653004-9598 ;. - Information systems engineering and management ;v. 1..

1.Quantum Machine Learning for Cost Variance Analysis in Industrial Manufacturing Processes: A Computational Breakthrough -- 2.Enhanced Optimization-Quantum Machine Learning -- 3.Industrial Automation and Challenges -- 4.A Hybrid Quantum-Classical LSTM approach for Predicting the Stock Market -- 5.Quantum Machine Learning Applications -- 15.Revolutionizing Pattern Recognition with Quantum Machine Learning -- 16.Quantum neural networks: from concept to simulation -- 17.Improved Pattern Recognition - Quantum Machine Learning -- 18.Understanding the Fundamentals of Quantum Computing.

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.

ISBN: 9783031997860

Standard No.: 10.1007/978-3-031-99786-0doiSubjects--Topical Terms:

1070322
Quantum computing.

LC Class. No.: QA76.889

Dewey Class. No.: 006.3843

Quantum machine learning in industrial automation
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520 $a 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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