Description

Research focus: Applications of quantum computing, quantum-enhanced AI, quantum computing for optimization & simulation problems, quantum-classical workflows, benchmarkingof quantum computing

For which applications will quantum computing be useful and how do we need to design quantum-classical workflows to integrate quantum computing into future hybrid quantum-enhanced data science workflows? My group is researching these questions, looking at potential near-term and future applications in both academia and industry. Examples include, e.g., the medical sector and the energy sector. We integrate quantum algorithms as subroutines into complex data science algorithms in machine learning, combinatorial optimization and to address simulation problems. To understand where and how quantum computing can be useful, we are experts on quantitative, application-centric benchmarking of quantum computers – comparing here also different quantum computing hardware modalities.

Selected Publications

• Bärligea, Adelina, Benedikt Poggel, and Jeanette Miriam Lorenz. "Scalability challenges in variational quantum optimization under stochastic noise." Physical Review A 112.3 (2025): 032407.
• Sakhnenko, Alona, Christian B. Mendl, and Jeanette M. Lorenz. "Is data-efficient learning feasible with quantum models?." arXiv preprint arXiv:2508.19437 (2025).
• Lorenz, Jeanette Miriam, et al. "Systematic benchmarking of quantum computers: status and recommendations." arXiv preprint arXiv:2503.04905 (2025).
• Drăgan, T. A., Maureen Monnet, Christian B. Mendl, and Jeanette Miriam Lorenz. "Quantum Reinforcement Learning for Solving a Stochastic Frozen Lake Environment and the Impact of Architecture and Optimizer Choices." ICAART 2023, 15th International Conference on Agents and Artificial Intelligence. Proceedings. Vol.2.
• Poggel, Benedikt, Nils Quetschlich, Lukas Burgholzer, Robert Wille, and Jeanette Miriam Lorenz. "Recommending solution paths for solving optimization problems with quantum computing." In 2023 IEEE International Conference on Quantum Software (QSW), pp. 60-67. IEEE, 2023.
• Matic, Andrea, Maureen Monnet, Jeanette Miriam Lorenz, Balthasar Schachtner, and Thomas Messerer. "Quantum-classical convolutional neural networks in radiological image classification." In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 56-66. IEEE, 2022.