Quantum machine learning

The Quantum machine learning course is designed to guide students through the complexities of integrating quantum computing and machine learning. The course containing 8 modules will elucidate the potential advantages and real-world applications of quantum machine learning, preparing learners to identify and leverage quantum computing solutions for complex business challenges. 

Learners will gain hands-on experience with formulating Quantum Unconstrained Binary Optimization (QUBO) and the Quantum Approximate Optimization Algorithm (QAOA), including the use of D-Wave systems for solving optimization problems. They will also learn to implement quantum machine learning models such as Parameterized Quantum Circuits (PQC) and Quantum Support Vector Machines, understanding the nuances of quantum algorithms and their industrial applications in machine learning tasks like classification, regression, and clustering.

Course Structure

1. Why Quantum Machine Learning?
2. Optimization Problems (QUBO+Dwave)
3. Parameterized Quantum Circuit (PQC)
4. QAOA in Optimization
5. Quantum Classifiers / Quantum Regression
6. Kernel method (Quantum Support Vector Machine)
7. Unsupervised QML (clustering)
8. Advanced algorithms, available computers and current challenges

Audience

Executives & Strategists, Non-Technical Managers, Engineers & Technicians, Software Developers and Data Scientists, Researchers, students, Quantum Enthusiast