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Quantum computing and quantum machine learning for engineers and developers

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantum computing and quantum machine learning for engineers and developers by Jesse Van Griensven Thé, Roydon Andrew Fraser, Jose Rosas-Bustos.
作者:	Van Griensven Thé, Jesse.
其他作者:	Fraser, Roydon Andrew.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xxxvii, 675 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Quantum computing.
電子資源:	https://doi.org/10.1007/978-3-031-98245-3
ISBN:	9783031982453$q(electronic bk.)

Quantum computing and quantum machine learning for engineers and developers
Van Griensven Thé, Jesse.

Quantum computing and quantum machine learning for engineers and developers [electronic resource] /by Jesse Van Griensven Thé, Roydon Andrew Fraser, Jose Rosas-Bustos. - Cham :Springer Nature Switzerland :2025. - xxxvii, 675 p. :ill., digital ;24 cm.

Introduction to Quantum Computing -- Fundamentals of Quantum Mechanics -- Quantum Algorithms -- Quantum Computing Hardware -- Introduction to Quantum AI -- Quantum Data Structures and Encoding -- Quantum Neural Networks -- Quantum Optimization Techniques -- Quantum Communication and Cryptography -- Tools and Platforms for Quantum Computing -- Industry Applications of Quantum Computing -- Future Trends in Quantum AI and Engineering.

This book guides readers from the foundations of quantum mechanics through advanced quantum algorithms (such as Shor's and Grover's) and state-of-the-art machine learning methods. By illustrating how these concepts apply to everyday engineering challenges, ranging from complex optimization and cryptography to high-fidelity simulations, the authors equip readers with the tools they need to develop and deploy quantum-based solutions. Incorporating practical case studies, industry-standard platforms, and tested pedagogical approaches, this resource speaks to both academic researchers and industry professionals, enabling them to seamlessly integrate quantum technologies into their projects and workflows. Draws on content rigorously tested at the PhD level, ensuring both scholarly depth and practical relevance; Features comprehensive coverage of quantum algorithms and machine learning methods tailored to engineering problems; Provides a hands-on, practice-oriented framework using industry-standard platforms: Qiskit, Cirq, and PennyLane.

ISBN: 9783031982453$q(electronic bk.)

Standard No.: 10.1007/978-3-031-98245-3doiSubjects--Topical Terms:

725269
Quantum computing.

LC Class. No.: QA76.889 / .V36 2025

Dewey Class. No.: 006.3843

Quantum computing and quantum machine learning for engineers and developers
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505 0 $a Introduction to Quantum Computing -- Fundamentals of Quantum Mechanics -- Quantum Algorithms -- Quantum Computing Hardware -- Introduction to Quantum AI -- Quantum Data Structures and Encoding -- Quantum Neural Networks -- Quantum Optimization Techniques -- Quantum Communication and Cryptography -- Tools and Platforms for Quantum Computing -- Industry Applications of Quantum Computing -- Future Trends in Quantum AI and Engineering.
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