Quantum Computing and Quantum Machine Learning for Engineers and Developers
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 crypt...
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| Autori principali: | , , |
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| Ente Autore: | |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edizione: | 1st ed. 2025. |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-031-98245-3 |
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| Riassunto: | 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. |
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| Descrizione fisica: | 1 online resource (XXXVII, 675 p. 73 illus.). 67 illus. in color. |
| ISBN: | 9783031982453 |
| DOI: | 10.1007/978-3-031-98245-3 |