Quantum Computing Algorithms for Artificial Intelligence

Explore the algorithms and hybrid systems where quantum computing meets artificial intelligence.

Quantum Computing Algorithms for Artificial Intelligence by Dr. Sri Amit Ray is a mathematically grounded 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.