Quantum Machine Learning.

Catalog description

Semester course; 3 lecture hours (delivered online or face-to-face). 3 credits. Enrollment is restricted to graduate students in the College of Engineering or by permission of the instructor. Introduction to quantum information processing: qubit, quantum gates, quantum measurement, quantum speedup, noise and fault tolerance. Introduction to machine learning algorithms: models, loss, optimization. Quantum procedures for machine learning: data input and output, quantum search, architectures of quantum models, gradient descent. Quantum optimization for convex models, quantum variational neural networks. Quantum-inspired algorithms: dequantization, compact representations, tensor networks.