Fraunhofer Institute for Industrial Mathematics ITWM
We offer a series of recurring training courses together with other Fraunhofer Institutes, the Fraunhofer Big Data and Artificial Intelligence Alliance and the Fraunhofer Academy. This page provides an overview of these activities:
UpQuantVal: Ein starkes Zentrum für Quantenforschung in der Oberrheinregion
© iStockphoto
UpQuantVal: Ein starkes Zentrum für Quantenforschung in der Oberrheinregion
© iStockphoto
UpQuantVal: Ein starkes Zentrum für Quantenforschung in der Oberrheinregion
© iStockphoto
UpQuantVal: Ein starkes Zentrum für Quantenforschung in der Oberrheinregion
© iStockphoto
UpQuantVal: Ein starkes Zentrum für Quantenforschung in der Oberrheinregion
© iStockphoto
Training Courses With a Focus on Quantum Computing
Further Training and Workshops
Quantum Computing Basics
Quantum computing and the underlying quantum mechanics open new doors. In the 1990s, Shor's algorithm demonstrated an exponential speed-up over classical algorithms for factoring large numbers. In recent years, the development of quantum computers is making great progress and we need to learn how to take advantage of quantum computing.
In this training you will learn the basics of quantum computing and the most important algorithms. The concepts will be taught with a variety of case studies from real applications and projects. One focus of the course is to directly deepen what you have learned with practical application examples.
Learning Objectives
The participants
- know the basic formal concepts of quantum computing (quantum state, bit vs. qubit, measurement)
- can name the important differences between quantum computing and classical computing
The participants can
- describe the basic quantum algorithms and their underlying intuitions
- describe current challenges in the development and implementation of basic quantum algorithms
- develop simple quantum programs
- analyze and modify complex quantum algorithms, such as Shor's algorithm
Certification
Certification (tested in accordance with ISO standard 17024 ) is carried out by the Fraunhofer Personnel Certification Authority. The certificate attests to the graduates' relevant innovative practical knowledge and proven competence.
Program
First part
- Overview of quantum computing, key concepts and application areas
- Fundamentals of quantum physics
- Mathematical foundations of quantum programming
Second Part
- Quantum gates and circuits, Bra-Ket formalism, Clifford gates
- Limitations of current quantum computers
Third Part
- Creating and simulating simple quantum programs
- Overview of important quantum algorithms
- Grover’s Algorithm
Fourth Part
- Quanten-Fourier-Transformation (QFT) and Quantum phase estimation (QPE)
- Shor's Algorithm
- Conclusions and discussion
Overview Four Days Course
Quantum Machine Learning
Quantum computing and machine learning are among the key technologies that will have a significant impact on digital development in the coming decades – and are already bringing about the first changes today. In order to achieve pioneering results in these key areas, we need specialists with in-depth knowledge in both disciplines.
The course covers topics at the interface of quantum computing and artificial intelligence. It is aimed at participants with experience in quantum computing as well as experts from the field of data science. You will acquire the skills to use machine learning algorithms on quantum computers in a targeted manner.
Therefore modern methods are presented that make it possible to utilize technological advances at an early stage and independently develop new approaches in Quantum Machine Learning (QML). The theoretical concepts are illustrated using numerous case studies from real applications. An essential part of the course is to further consolidate the acquired knowledge through practical examples.
Learning Objectives
The participants
- 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
The participants
- can describe the Quantum Support Vector Machine method and use it in application cases
- understand the strengths, weaknesses and limitations of current QML procedures
The participants
- can read quantum circuits and create them independently
- are able to encode data on the quantum computer and then analyze the encoding,
- can apply hybrid quantum-classical optimization algorithms (e.g. Variational Quantum Eigensolver (VQE) and Quadratic Unconstrained Binary Optimization (QUBO))
- are able to create quantum clustering algorithms and implement them in practical examples
Quantencomputing Machine Learning
Certification
Certification (tested in accordance with ISO standard 17024 ) is carried out by the Fraunhofer Personnel Certification Authority. The certificate attests to the graduates' relevant innovative practical knowledge and proven competence.
Program
First Module
- Part One: Fundamentals of Machine Learning and Data Science
- Data preprocessing
- Feature spaces
- Supervised Learning, Unsupervised Learning
- Exemplary challenges: Classification and clustering
- Complexity
- Rating
- Function rooms
- Second Part: Quantumcomputing
- Basic theoretical concepts
- Different paradigms: Quantum Gate and Adiabatic
- Quanten-Fourier-Transformation
- Quadratic Unconstrained Binary Optimization (QUBO)
- Advantages over traditional methods
Second Module
- Parameterized quantum circuits
- Data coding
- Analysis of parameterized quantum circuits
Third Module
- Clustering required for quantum computing
- Grover-Algorithm
- Quantum k-Means
- SWAP-Test
Fourth Module
- Classic support vector machines and kernel trick
- Quanten-Feature-Maps
- Training of quantum kernels, kernel alignment
- Kernel-based training versus variation training in relation to circuit evaluations
Fifth Module
- Neural networks
- Quantum neural networks (QNNs)
- Use cases of QNNs
- Possible quantum advantages of QNNs
Quantum Image Processing
In the »Quantum Image Processing« focus area, we investigate the extent to which quantum computers (QC) solve classical image processing problems. In addition to the theoretical results, the focus is on the practical implementation on current hardware and the identification of new possibilities.
We start from conventional images, so the classical image information must be encoded in quantum states. To explore the current possibilities, we conducted experiments on quantum computer simulators and on real quantum computers: We converted classical images into quantum states and then back into classical images. In these experiments, we were able to practically demonstrate the theoretically predicted advantage of an exponentially smaller number of qubits (quantum bits) compared to classical bits.
Program
- Quantum image coding method
- Basic coding
- Phase coding
- Amplitude coding
- Hybrid quantum-classical approach
- Quantum transfer learning for image classification
- Solving image processing tasks with quantum computing on NISQ quantum hardware
- Angle estimation with quantum Fourier transform
- Edge detection with artificial quantum neurons
- Image classification
- Quantum Transfer Learning (hybrides quantenklassisches Approach)
- Quanten-Support-Vektor-Maschine (QSVM)
- Variational Quantum Linear Solver (VQLS)
- Image segmentation
- Quantum Annealing
- Quanten-Clustering, z. B. Quanten-k-Means
Quantum Image Processing