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Quantum Machine Learning

In this course, you learn the essentials of Quantum Machine Learning.

About This Course

In this course, participants learn the essentials of Quantum Computing. We start by outlining the conceptual foundations of quantum systems. The next chapter focuses on the basic elementary computational operations, with example programs in Python qiskit. Using these building blocks, we introduce some of the core quantum computing algorithms, with a focus on coherent quantum machine learning. Examples are Quantum Fourier Transformation, Quantum Phase Estimation and Grover search. Next, we introduce the concept of Near-Term Intermediate Scale Quantum devices (NISQ) and derive hybrid quantum-classical algorithms that are suitable for running on current hardware. Hereby, we also discuss the recent breakthroughs and quantum supremacy, and discuss existing hardware. Quantum computing intuition often requires some mathematical intuition. The course requires a medium to strong theoretical background, but is accessible for people who are unfamiliar with quantum mechanics and quantum computing. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details.

See this sample lecture video on YouTube to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at:


The enrollment fee for this course is EUR 100 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, code scripts, quizzes and certificate).


Before subscribing to this course, you should have a basic understanding of linear algebra (e.g., vectors, matrices, tensors) and complex calculus (e.g., complex conjugates). You should also have followed and completed our Machine Learning Essentials course.

Course Outline

  • Introduction
    • Instructor Team
    • Our Publications
    • Course Outline
    • Software
    • Python Tutorial
    • Disclaimer
  • Chapter 1: Introduction to quantum mechanics
    • Why Quantum Computing
    • History of Quantum Physics
    • Qubits
    • Probabilistic interpretation
    • Bloch sphere
    • Interference
    • Two Qubit states
    • Entanglement
    • Qubits versus Bits
    • Quiz
  • Chapter 2: Introduction to Quantum Computing
    • Forms of quantum computing
    • Quantum Computing: Abstraction Levels
    • Quantum Circuit
    • Quantum Operators
    • Single-qubit operators
    • Multi-qubit operators
    • The Power of Quantum
    • Note from the Lecturer
    • Grover’s algorithm
    • Quiz
  • Chapter 3: Storing Data on a Quantum Computer
    • Data Representations
    • Basis encoding
    • Amplitude encoding
    • Dynamic encoding
    • Quiz
  • Chapter 4: Coherent Quantum Computing
    • Quantum Fourier Transformation
    • Quantum Phase Estimation
    • Expectation Values
    • Matrix Multiplication (HLL)
    • Quantum Random Access Memory
    • Quantum Support Vector Machines
    • Adiabatic quantum computing
    • Quiz
  • Chapter 5: Algorithms for Current and Near-Term Quantum Computing Devices
    • Decoherence
    • Quantum supremacy
    • NISQ
    • Quantum Approximate Optimization Algorithm (QAOA)
    • Variational Quantum Linear Solver
    • Quantum Annealing
    • Cloud Quantum Computing
  • Quiz

Course Staff

dr. Janneys Nys

Jannes Nys

Dr. Jannes Nys was born in Oudenaarde (East-Flanders, Belgium) on May 5th, 1991. He lives in the beautiful city of Ghent, where he enjoys a beer with a view of the water. He loves sunny Sunday BBQs, and traveling with his partner Sofie. Jannes obtained his PhD in physics at Ghent University in Belgium. He studied Artificial Intelligence at KULeuven, where he started his research on Quantum Machine Learning. After his PhD, Jannes worked as head of machine learning and data science at Boltzmann, a Ghent-based machine learning company. There, he led a team of data scientists working on custom machine learning solutions for the industry. He currently works as a post-doctoral research fellow at Ghent University. His main research focuses on the intersection between machine learning and physics.

Prof. dr. Bart Baesens

Bart Baesens

Bart was born in Bruges (West Flanders, Belgium) on February 27th, 1975. He speaks West-Flemish (which he is very proud of!), Dutch, French, a bit of German, some English and can order a beer in Chinese. He is married to Katrien Denys and has 3 kids (Ann-Sophie, Victor and Hannelore), and 2 cats (Felix and Simba). Besides enjoying time with his family, he is also a diehard Club Brugge soccer fan. Bart is a foodie and amateur cook. He loves drinking a good glass of wine (his favorites are white Viognier or red Cabernet Sauvignon) either in his wine cellar or when overlooking the authentic red English phone booth in his garden. His favourite pub is “In den Rozenkrans” in Kessel-Lo (close to Leuven) where you will often find him having a Gueuze Girardin 1882 or Tripel Karmeliet with a spaghetti of the house. Bart loves traveling and his favorite cities are: San Francisco, Sydney and Barcelona. He is fascinated by World War I and reads many books on the topic. He is not a big fan of being called professor Baesens (or even worse, professor Baessens), shopping (especially for clothes or shoes), pastis (or other anise-flavored drinks), vacuum cleaning (he can’t bare the sound), students chewing gum during their oral exam of Credit Risk Modeling (or had garlic for breakfast), long meetings (> 30 minutes), phone calls (asynchronous e-mail communication is a lot more efficient!), admin (e.g., forms and surveys) or French fries (Belgian fries are a lot better!). He is often praised for his sense of humor, although he is usually more modest about this. Bart is also a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on Big Data & Analytics, Credit Risk Modeling, Fraud Detection and Marketing Analytics. He has written more than 250 scientific papers, some of which have been published in well-known international journals (e.g., MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at top international conferences (e.g., ICIS, KDD, CAISE). He has received various best paper and best speaker awards. Bart is the author of 8 books: Credit Risk Management: Basic Concepts (Oxford University Press, 2009), Analytics in a Big Data World (Wiley, 2014), Beginning Java Programming (Wiley, 2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (Wiley, 2015), Credit Risk Analytics (Wiley, 2016), Profit Driven Business Analytics (Wiley, 2017), Web Scraping for Data Science with Python (Apress, 2018), and Principles of Database Management (Cambridge University Press, 2018). He sold more than 25.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean. His research is summarized at For an overview of the courses he is teaching, see He also regularly tutors, advises and provides consulting support to international firms regarding their big data, analytics and credit risk management strategy.