Certified Data Scientist Specialized in Quantum Machine Learning

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.

Overview of the Training »Certified Data Scientist Specialized in Quantum Machine Learning«

Format Online and in-person
Duration 5 units (7 training days + exam)
Schedule

Unit 1 Theory (online): May 5, 2025 (09:00–12:30)

Unit 1 Theory (online): May 7, 2025 (09:00–12:00)

Unit 1 Hands-On (Fraunhofer ITWM Kaiserslautern): May 12, 2025

Units 2–5 (Fraunhofer ITWM Kaiserslautern): May 13–16, 2025

Certification Exam (online): May 28, 2025

Certification The certification is provided by the Fraunhofer Personnel Certification Office. The certificate confirms relevant, innovative practical knowledge and proven expertise..
Requirements for Certification A degree or equivalent qualification through individual proof.
Aims of the Training

Aims of the Training

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

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

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

All Units at a Glance

Unit 1 - Part 1

Basics of Machine Learning/Data Science

  • Data preprocessing
  • Feature spaces
  • Supervised learning, unsupervised learning
  • Exemplary problems: classification, clustering
  • Complexity
  • Evaluation

Unit 1 - Part 2

Quantumcomputing

  • Basic theoretical concepts
  • Different paradigms: Quantum Gate and Adiabatic
  • Quantum Fourier transform
  • Quadratic Unconstrained binary optimization (QUBO)
  • Advantages over classical

Unit 2

  • Parametrized quantum circuits
  • Data encoding
  • Analyzing parametrized quantum circuits

Unit 3

  • Clustering needed for quantumccomputing
  • Grover algorithm
  • Quantum k-Means
  • SWAP test

Unit 4

  • Classical support vector machines and kernel trick
  • Quantum feature maps
  • Train quantum kernels, kernel alignment
  • Kernel based versus variational training in terms of circuit evaluations

Unit 5

  • Neural networks
  • Quantum neural networks (QNNs)
  • Use cases of QNNs
  • Potential quantum advantages of QNNs
  • Informationen zu den Schulungsdozent*innen finden Sie hier.

Contact

Contact Press / Media

Anne Halbich

Fraunhofer Institute for Open Communication Systems
Kaiserin-Augusta-Allee 31
10589 Berlin, Germany

Phone +493034637346

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