Quantum Machine Learning for Engineering and Sciences Applications

This course offers a theoretical and hands-on introduction to Quantum Computing for modeling and implementing solutions for engineering and sciences applications. Given the increasing complexity of modern engineering systems, fueled by the global trend towards industry 4.0 and the immense amount of information that will be necessary to store, maintain and process to operate future systems, researchers and practitioners safely and efficiently have shift their attention towards explore and leverage the potential of quantum computers as a path to achieve diagnostics and prognostics solutions that are exponentially faster that those existing today. This course is designed to be the first introduction to the area of quantum computing by delivering the main topics required to students and practitioners to master and access the real potential that quantum computing can bring to engineering and sciences applications. Students will be exposed to the theory and concepts underpinning quantum computing and quantum machine learning, understand the main hardware frameworks under which quantum processors are built, develop quantum machine learning algorithms on quantum simulators and understand the current landscape of quantum computing. Focus is placed on learn-by-doing, using quantum computer frameworks built in Python to develop algorithms that can be executed using real-world data from varying reliability and risk contexts.