krishnakumarsekar/awesome-quantum-machine-learning

Organizes foundational quantum computing concepts (qubits, superposition, entanglement, quantum gates) alongside machine learning algorithm implementations including quantum K-means, support vector machines, and neural network variants. The resource maps the mathematical bridge between quantum mechanics and ML through tensor networks, Hilbert spaces, and quantum Fourier transforms, providing visual architecture diagrams and theoretical foundations. Covers both gate-based quantum computing approaches and quantum annealing frameworks, with links to implementations across multiple quantum platforms and classical simulation libraries.

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