Quantum computing and machine learning are key technologies that will significantly change our technological landscape in the coming decades, and in some cases are already doing so today. The module covers topics at the intersection of quantum computing and machine learning and teaches how to put them into practice.
... 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.
... can describe the Quantum Support Vector Machine method and use it in application cases.
... be able to formulate the strengths, weaknesses and limitations of current QML methods.
| ORGANIZER | Fraunhofer ITWM Fraunhofer FOKUS Fraunhofer IAIS Fraunhofer IPA Fraunhofer FIT |
|---|---|
| EVENT TYPE | Training course |
| EVENT LOCATION | Fraunhofer FOKUS Kaiserin-Augusta-Allee 31 10589 Berlin |
| Format | Presence + Online |
| LANGUAGE | English |
| COURSE DURATION/DAILY SCHEDULE | Online:
Presence:
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| ACCESS REQUIREMENT | Previous experience in the field of data science / machine learning or quantum computing or similar previous experience (can also be clarified individually) |
| BRIEF DESCRIPTION OF THE CONTENTS |
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Fraunhofer Academy
