東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Quantum machine learning in industrial automation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantum machine learning in industrial automation/ edited by Anupam Ghosh ... [et al.].
other author:	Ghosh, Anupam.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	x, 456 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Quantum computing. -
Online resource:	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. 65..

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:

2115803
Quantum computing.

LC Class. No.: QA76.889

Dewey Class. No.: 006.3843

Quantum machine learning in industrial automation
LDR:03680nmm a2200337 a 4500 001 2414338
003 DE-He213
005 20250902130245.0
006 m d
007 cr nn 008maaau
008 260205s2025 sz s 0 eng d
020 $a 9783031997860 $q (electronic bk.)
020 $a 9783031997853 $q (paper)
024 7 $a 10.1007/978-3-031-99786-0 $2 doi
035 $a 978-3-031-99786-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.889
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3843 $2 23
090 $a QA76.889 $b .Q1 2025
245 0 0 $a Quantum machine learning in industrial automation $h [electronic resource] / $c edited by Anupam Ghosh ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a x, 456 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Information systems engineering and management, $x 3004-9598 ; $v v. 65
505 0 $a 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.
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.
650 0 $a Quantum computing. $3 2115803
650 0 $a Machine learning. $3 533906
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Engineering. $3 3409361
650 2 4 $a Quantum Computing. $3 1620399
700 1 $a Ghosh, Anupam. $3 3790972
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Information systems engineering and management ; $v v. 65. $3 3790973
856 4 0 $u https://doi.org/10.1007/978-3-031-99786-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)