Quantum Computing Algorithms for Artificial Intelligence (Explore Quantum Machine Learning) - Tapa blanda

Sinopsis

Explore the Quantum machine algorithms in deep and explore the hybrid systems where quantum computing meets artificial intelligence.

Quantum Computing Algorithms for Artificial Intelligence by Dr. Sri Amit Ray is a guide to the algorithms, learning models, and hybrid systems at the intersection of quantum computing and AI. It builds from quantum foundations to current research, with a practical focus on data access, classical baselines, resource costs, sampling, compilation, and noise.

Inside the book:

• Qubits, gates, circuits, measurement, and the classical machine-learning baselines quantum methods must be compared against
• Core algorithms including amplitude amplification, the quantum Fourier transform, phase estimation, quantum linear algebra, annealing, quantum Monte Carlo, and quantum walks
• Variational and hybrid methods: VQE, QAOA, data re-uploading, quantum neural networks, gradient estimation, and trainability
• Quantum learning models: kernel methods and support vector machines, quantum extreme learning machines and reservoir computing, quantum PCA, generative models, and quantum reinforcement learning
• Quantum architecture search, software automation, error mitigation, AI-assisted error correction, hybrid system design, benchmarking, and the latest developments
• Applications in optimization, finance, chemistry, and quantum sensing

Who this book is for:

• AI, machine learning, and data science professionals
• Researchers and graduate students in quantum computing and computer science
• Engineers and technology leaders planning for the quantum era
• Curious readers who want a rigorous yet accessible entry into quantum AI

Learn through worked examples. Numerical results, from Grover search and QAOA to molecular energies, barren-plateau analysis, and reservoir computing, are accompanied by code in Appendix C so you can reproduce and adapt the book's figures and examples. The book also includes exercises with selected answers, a mathematical workshop, an algorithm selection guide, a glossary, and references.

Written for graduate students, researchers, engineers, and technically minded practitioners with a background in linear algebra, probability, and basic machine learning. No prior quantum mechanics is assumed.

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