Data Scientist Specialized in Quantum Machine Learning

With the "Data Scientist Specialized in Quantum Machine Learning" certificate, we certify practical knowledge of how to successfully apply machine learning with quantum computers.

A certified "Data Scientist Specialized in Quantum Machine Learning"

• knows the basic formal concepts of quantum computing (quantum state, bit vs. qubit, measurement),
• knows the basic formal concepts of machine learning (objective function, model class, cross-validation, kernel function),
• can use ideas and building blocks of quantum algorithms for QML problems,
• can describe the Quantum Support Vector Machine method and use it in applications,
• understand the strengths, weaknesses and limitations of current QML methods,
• can read quantum circuits and create them independently,
• can encode on the quantum computer and subsequently analyze the encoding,
• can apply hybrid quantum-classical optimization algorithms (e.g. Variational Quantum Eigensolver (VQE) and Quadratic Unconstrained Binary Optimization (QUBO))
• and is able to create quantum clustering algorithms and implement them in practical examples.

Target group

• Professionals from the fields of data science and machine learning
• Employees of technology companies, such as pharmaceutical and chemical companies
• Employees of government agencies who are 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

• Data preprocessing
• Feature spaces
• Supervised learning, unsupervised learning
• Exemplary problems: classification, clustering
• Complexity
• Evaluation
• Basic theoretical concepts
• Different paradigms: Quantum Gate and Adiabatic
• Quantum Fourier transform
• Quadratic Unconstrained binary optimization (QUBO)
• Advantages over classical
• Clustering needed for quantumccomputing
• Grover algorithm
• Quantum k-Means
• SWAP test
• Parametrized quantum circuits
• Data encoding
• Analyzing parametrized quantum circuits
• 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
• Neural networks
• Quantum neural networks (QNNs)
• Use cases of QNNs
• Potential quantum advantages of QNNs

Das Zertifikat »Data Scientist Specialized in Quantum Machine Learning«  orientiert sich an den empfohlenen Richtlinien des European Competence Framework for Quantum Technologies und deckt die geforderten Kompetenzen ab.