Quantum Machine Learning

About the course

This online course on Quantum Machine Learning (QML) is designed to explore the intersection of quantum computing and machine learning, highlighting the unique advantages and potential applications of merging these two cutting-edge fields. Starting with the context and motivation behind Quantum Machine Learning, the course outlines its potential advantages and applications, aiming to mold learners into skilled Quantum Engineers. Through a structured curriculum, students will dwelve into:

• Optimization problems using Quantum Annealing,
• Parameterised Quantum Circuits (PQC), and Quantum Approximate Optimization Algorithm (QAOA), along with
• Advanced modeling and applications in quantum classifiers, regression, and unsupervised learning methods like clustering.

Each module includes hands-on exercises to reinforce learning, progressing towards understanding advanced algorithms, the current quantum computing landscape, and ongoing challenges in the field. This course is designed to equip learners with the knowledge and skills to apply quantum computing concepts in machine learning to solve real-world problems.

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Skills you will learn

• Technical Skills: Participants 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.
• Business Skills: The course 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. Additionally, through understanding the strategic importance of quantum-enhanced algorithms and their ability to outperform classical counterparts in specific tasks, students will be equipped to make informed decisions regarding investment in quantum technologies and innovation in their respective fields.

Course Structure

• Lesson 0: Why Quantum Machine Learning?
• Lesson1: Optimization Problems (QUBO+Dwave)
• Lesson2: Parameterized Quantum Circuit (PQC)
• Lesson 3: QAOA in Optimization
• Lesson 4: Quantum Classifiers / Quantum Regression
• Lesson 5: Kernel method (Quantum Support Vector Machine)
• Lesson 6: Unsupervised QML (clustering)
• Lesson 7: Advanced algorithms, available computers and current challenges

Course Prerequisites

All potential learners must have expertise in python and prior knowledge or familiarity with basic quantum algorithms/basic quantum programming.

Duration

The estimated duration to complete this course is approximately 25 hours.