Master Thesis in Quantum-Assisted Optimization for the Automation of Accounting

Our team identifies relevant technological innovations from science, research and technology partners in order to make them usable for the product teams at an early stage. For this reason, we participate in research projects as an industry partner in areas such as quantum computing in the BMWK project QuaST.
Quantum Computing is a novel, disruptive technology with the potential to tackle challenging optimization problems. These arise in many areas of interest – logistics, network problems, robustness verification. You will research the applications of current noisy intermediate-scale quantum (NISQ) devices and algorithms for optimization use cases.

These are the tasks:

• Within the thesis you will work on the automated generation of accounting statements and use available graphical models, which have been trained on data.
• You will apply the known representation of Bayesian Networks by Quantum Circuits and investigate the inference via Quantum Rejection Sampling.
• Furthermore, you will investigate the feasibility of quantum algorithms to train models based on data represented in a Knowledge Graph.
• A close collaboration with Fraunhofer IKS is intended.

Skills

• You would like to do your master's thesis in physics, computer science or a related field
• Basic knowledge in quantum computing
• Basic knowledge in mathematical optimization and/or machine learning
• Programming skills (preferably Python, Qiskit or Pennylane)
• Interest in applied research and the transfer of state-of-the-art methods to relevant applications