Books
Nielsen and Chuang remains the classic foundation. Yanofsky and Mannucci is a gentler computer science entry point, while Wittek and Schuld focus more directly on QML.
AI for Science Notes
Quantum machine learning resources.
A compact reference page for people exploring the intersection of quantum computing and machine learning, with starting points across textbooks, lectures, software, groups, and topic maps.
Talks and courses
Maria Schuld, Kyle Cranmer, and Perimeter Institute talks are strong overview material. MIT, Caltech, and CMU course notes remain useful for quantum fundamentals.
Tools and frameworks
IBM’s ecosystem, Microsoft Q#, and cloud access from major providers are practical places to get hands-on quickly.
- Nielsen and Chuang, Quantum Computation and Quantum Information
- Yanofsky and Mannucci, Quantum Computing for Computer Scientists
- Peter Wittek, Quantum Machine Learning
- Maria Schuld, Supervised Learning with Quantum Computers
Topic Map
Research directions worth tracking.
Model classes
Quantum neural networks, quantum reinforcement learning, quantum Bayesian methods, and quantum probabilistic graphical models.
Scientific links
Many-body systems, phase transitions, measurement learning, state estimation, and active learning for quantum experiments.
Theory links
Quantum learning theory, sampling, Hilbert space views of learning, uncertainty principles, and links to cognition.