Quantum computing and machine learning are key technologies that will significantly shape our technological landscape in the coming decades, and in some cases are already doing so today. In order to achieve competitive results in these fields, highly qualified experts with expertise in both areas are required. The module covers topics at the intersection of quantum computing and machine learning. It is aimed at people with a quantum computing background as well as people with a background in data science. Participants will gain the ability to successfully apply machine learning with quantum computers. To this end, numerous current methods are presented that enable them to react to future hardware advances and independently develop new QML algorithms. The concepts taught are illustrated with a large number of case studies from real applications and projects. A large part of the course is dedicated to consolidating what has been learnt with practical application examples.

Target Group

• Experts from the fields of data science and machine learning
• Employees of technology companies, such as pharmaceutical and chemical companies
• Employees of government agencies interested in potential applications in the fields of cryptography and cyber security 
• Employees of research institutions and students pursuing a master's degree or doctorate in fields such as computer science, physics, mathematics or data science who would also like to update their knowledge of QML
• Employees of research institutions and students who have previous experience in the field of quantum computing

Aims of the training

Learning objectives

The particpants...

• know the basic formal concepts of quantum computing (quantum state, bit vs. qubit, measurement)
• know the basic formal concepts of machine learning (objective function, model class, cross-validation, kernel function)
• learn to use ideas and building blocks of quantum algorithms for QML problems

Knowledge / Understanding

The particpants...

• can describe the Quantum Support Vector Machine method and use it in application cases
• understand the strengths, weaknesses and limitations of current QML procedures

Skills
The particpants...

• can read quantum circuits and create them independently
• are able to encode data on the quantum computer and subsequently analyse the encoding,
• are able to apply hybrid quantum-classical optimisation algorithms (e.g. Variational Quantum Eigensolver (VQE) and Quadratic Unconstrained Binary Optimisation (QUBO)),
• are able to create quantum clustering algorithms and implement them in practical examples

Certification

Certification is carried out by the Fraunhofer Personnel Certification Authority. The certificate (according to EN ISO 17024) attests to the graduates' relevant innovative practical knowledge and proven expertise.

Requirements for certification

Degree or equivalent qualification by individual proof.

Language

English

Schedule

Dates will follow soon.