Quantum Machine Learning

• Quantum computing basics: qubits, superposition, entanglement, measurement
• Quantum gates: Pauli (X, Y, Z), Hadamard, CNOT, Toffoli, rotation gates
• Quantum circuits: circuit model, parameterised circuits, depth and width
• Variational quantum algorithms: VQE, QAOA, variational classifiers
• Quantum kernel methods: quantum feature maps, quantum support vector machines
• Quantum neural networks: parameterised quantum circuits as neural layers
• Barren plateaus: vanishing gradients in quantum circuits, expressibility vs trainability
• Quantum advantage debate: NISQ era limitations, fault-tolerant quantum computing timeline
• Hybrid classical-quantum architectures: quantum layers in classical pipelines