Torrent details for "Van Griensven T. Quantum Computing and Quantum Machine Learning.…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
16.0 MB
Info Hash:
6D296BCD346683228F86A466AD8CB7F904654072
Added By:
Added:
Oct. 27, 2025, 4:08 p.m.
Stats:
|
(Last updated: Oct. 27, 2025, 4:10 p.m.)
| File | Size |
|---|---|
| Van Griensven T. Quantum Computing and Quantum Machine Learning...2025.pdf | 16.0 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Van Griensven T. Quantum Computing and Quantum Machine Learning...2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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 workflow. This book is structured into clear, concise chapters, each building logically upon the preceding ones: Chapter 1: Introduction to Quantum Computing provides foundational knowledge, covering the evolution and core principles of quantum computing, essential linear algebra concepts, and the signi fi cance of quantum mechanics for engineers. Chapter 2: Quantum Computing Fundamentals introduces wave-particle duality, uncertainty principles, Schrödinger equation, qubits, quantum gates, and quantum measurement concepts necessary for understanding quantum computations. Chapter 3: Quantum Circuits discusses quantum programming languages and platforms, such as Qiskit, Google ’ s Cirq, Microsoft ’ s Q#, and quantum simulators, emphasizing quantum hardware architectures, error correction, and fault tolerance. Chapter 4: Quantum Algorithms details critical quantum algorithms including Grover ’ s search, quantum Fourier transform, Shor ’ s factoring algorithm, and the variational quantum eigensolver (VQE), outlining their practical engineering applications. Chapter 5: Classical Artificial Intelligence outlines core concepts in classical AI, including supervised and unsupervised learning, neural networks, optimization techniques, and their signi fi cance for engineers transitioning to quantum AI. Chapter 6: Quantum Data Structures examines efficient data encoding methods crucial for quantum computing, highlighting quantum encoding techniques, noise mitigation, and challenges related to quantum data representation. Chapter 7: Quantum Optimization explores quantum optimization methods like quantum approximate optimization algorithm (QAOA), quantum kernels, and quantum classi fi ers and compares quantum optimization to classical methods, detailing relevant engineering applications. Chapter 8: Introduction to Quantum AI introduces quantum-enhanced AI methods, emphasizing quantum-enhanced algorithms, hybrid quantum-classical approaches, quantum data processing, and specific applications in finance, optimization, and generative modeling. Chapter 9: Quantum Neural Networks (QNNs) discusses the motivation, evolution, and practical implementation of quantum neural networks, covering quantum perceptrons, quantum neurons, activation functions, training methods, and hybrid quantum-classical architectures
×