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Quantum Machine Learning for Business

Certificate Programme


Be an innovator in an aspiring and developing field – apply quantum machine learning to your field of practice!

This programme aims to introduce you to quantum machine learning, focusing on what might become practical applications in the industry. In the class, you will learn the basics of quantum computation and how it can be applied to machine learning to achieve faster and more effective algorithms.  The currently strongest use cases from finance, software and pharma as well as the chemical industry will be presented and discussed with the focus on practical applications in the industry.

The programme shows how technical components can be considered when using quantum machine learning and how hardware can be implemented for its application.

After the class, you will be able to judge where applications quantum machine learning algorithms will be useful, and where they are not. You will also be able to implement your own quantum computation and quantum machine learning algorithms on a quantum computer or a classical computer to build basic quantum machine learning algorithms.

Your Advantages

  • Interactive “lab” sessions
  • Immediate application with daily programming assignments
  • Individual support
  • Valuable networking within the circle of participants
  • Three trainers who are specialists in different areas of expertise
  • Recognised certificate after passing the programming assignments
  • Individual support and personal coaching
  • Develop relevant code for your own business context

Programme Content

The programme is designed to provide participants with in-depth technical knowledge as well as current use cases from the industry. The latest discourse and development state, as well as possibilities of implementation in an industrial context, will be shown.

A hands-on learning experience with direct support is the central core of the programme. Besides interactive lectures, reflection and discussion, there will be programming exercises every day to gain experience immediately and strengthen one's programming skills. Three lecturers from different departments will accompany the course. This creates the possibility of direct and individual support.

The Quantum Machine Learning for Business programme consists of a 6-day week with daily programming assignments and a final assignment. The course is held in English.

Theoretical background (Module 1)

The participant will learn about the theoretical background of quantum informatics. The math part starts with complex numbers and discusses the necessary parts of linear algebra. The physics part gives an introduction into quantum mechanics: the two slit experiment and the rules of measurement. Using the above, the basic building block of quantum informatics (qubit) is defined, and the application of the measurement rule discussed.

Module content:

  • Math basics
  • the basic building block of quantum informatics (qubit)
  • measurement

Multi qubit systems (Module 2)

The basics of quantum informatics are continued. The participant will learn about multi-qubit systems and how to entangle. When measuring these systems, the power of quantum computation is revealed. The module will finish with a basic introduction to the theoretical background of multi qubit systems.

Module content:

  • multi-qubit systems
  • Entanglement
  • theory support

Quantum optimization (Module 3)

The participant will learn about two different approaches of extremum search with a quantum computer. Adiabatic quantum computation is presented on D-Wave's model and the QAOA algorithm on a general quantum hardware.

Module content:

  • two different approaches of extremum search with a quantum computer
  • adiabatic quantum computation and the QAOA algorithm

Quantum neural networks (Module 4)

The participant will learn how to use and program quantum neural networks in an example. The end of the module will show how to deal with the problem of how the quantum computer interacts with the environment. Quantum error correction will show some basic ideas how these can be handled.

Module content:

  • using and programming neural networks
  • quantum error correction

Quantum GAN (Module 5)

The participant will learn to use the quantum GAN library IBM's qiskit framework on an example. A financial application on option pricing is discussed.

Module content:

  • the quantum GAN library IBM's qiskit framework
  • financial application on option pricing

Developement of use cases for your business (Module 6)

In the last module we discuss business applications of quantum computation and quantum machine learning, helping you to further understand relevant applications for your business and to develop use cases.

Module content:

  • Recent advancements and final programming assignment
  • business applications of quantum computation and quantum machine learning
  • use cases

Final exam

The certification is given when all programming assignments have been successfully completed. The participants receive the assignments daily during the course and start them on site.

After the 6-day course, the participants have two weeks to finalize their assignments and to submit them electronically.

Key Facts


Certification in Quantum Machine Learning for Business
(Frankfurt School of Finance and Management)

Target Group

Experts in data science/ machine learning, innovation managers, risk managers, IT strategists, financial analysts and experts in machine learning, as well as research scientists who would like to learn more about quantum machine learning.

The class is tailored to anyone from industry and research who wants to gain an understanding of quantum machine learning and how it can be applied to their field of practice.


  • Participants need to have a basic understanding of machine learning and programming skills in Python.
  • Whilst mathematical rigour is not our main focus, you should also feel comfortable in reading and interpreting mathematical formulas. Math handouts and exercises will be distributed before the class starts so that you can familiarise yourself with this respective material.
  • All participants should bring laptops with them to work on practical exercises during the course.


1 Week

Module 1

 27.3. 2023

Module 2


Module 3


Module 4


Module 5


Module 6



Total fee: EUR 5,900 (6 days).
The fee covers course registration (EUR 100), the examination (EUR 450) and final certification.
Course fees are exempt from VAT.


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Your Experts for Quantum Machine Learning

Professor Dr. Florian Ellsaesser

Prof. Dr. Florian Ellsäßer

is Professor at the Frankfurt School of Finance & Management. He completed his undergraduate degree in Economics and Philosophy in the UK and also holds a degree in mathematics and a masters and PhD from Cambridge University. For one of his research papers Florian received the Oxford, Cambridge, Warwick best PhD paper award. After his PhD Florian first worked as a strategy consultant and then as a project manager at McKinsey & Company. At the Frankfurt School he is focused on the application of machine learning to management problems in the areas of marketing and strategy. He teaches deep learning, natural language processing and reinforcement learning and quantum machine learning.

Levente Szabados

Levente Szabados is co-founder and lead consultant at Neuron Solutions. With 10+ years experience in AI / Machine Learning field, including quantum machine learning, leading roles in multiple award winning startups and 5+ years of consulting experience, he is helping clients in applying cutting edge ML techniques to business challenges.

Levente Szabados


Dr. Balázs Bódi

is an AI developer at 3dHistech, ML / QML consultant at Neuron Solutions. Before working on quantum machine learning, he recieved a scholarship as a postdoc at the Wigner Research Center, harnessing quantum physics to develop more effective lasers.

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