Email: Stefanie(dot)Czischek(at)uOttawa(dot)ca
Research Interests
Our research focuses on the intersection of artificial neural networks and quantum technologies. We use machine learning methods to enhance classical numerical simulations of quantum many-body systems and to optimize processes in experimental setups for quantum computation and quantum simulation. Our special interest lies in exploring the power of implementations of artificial neural networks in spiking and non-spiking neuromorphic hardware or general physical systems in the context of quantum physics. The support of numerical methods with such analog implementations promises significant advances beyond the limitations of conventional computers.
Selected topics- Numerical studies of quantum many-body systems
- Artificial neural network quantum state representations
- Spiking neuromorphic hardware to advance classical simulations of quantum systems
- Artificial neural networks and analog implementations to optimize quantum experiments
Bio
Since 2022Assistant Professor at the Department of Physics, University of Ottawa (Canada).
2020 - 2022Postdoctoral Fellow at the Department of Physics and Astronomy, University of Waterloo (Canada), in Roger Melko's Perimeter Institute Quantum Intelligence Lab (PIQuIL). Research on automated tuning for quantum dot devices, measurement-induced phase transitions, and enhancement of variational Monte Carlo with quantum data.
2019 - 2020Postdoctoral Fellow at the Kirchhoff Insitute for Physics, Heidelberg University (Germany), with Thomas Gasenzer and Martin Gärttner. Research on representing strongly entangled quantum states with the BrainScaleS-2 spiking neuromorphic chip.
2020Springer Thesis Award recognizing outstanding Ph.D. research.
2016 - 2019 Ph.D. student at the Kirchhoff Institute for Physics, Heidelberg University, under the supervision of Thomas Gasenzer. Research on simulations of quantum dynamics with artificial neural networks and the discrete truncated Wigner approximation, and the sampling of quantum states with spiking neural networks. 2013 - 2016Master of Science at the Kirchhoff Institute for Physics, Heidelberg University, under the supervision of Thomas Gasenzer. Research on exploring the power and limitation of the discrete truncated Wigner approximation to simulate quantum critical dynamics.
2014 - 2015Erasmus semester at Uppsala Universitet (Sweden).
2010 - 2013Bachelor of Science at Heidelberg University under the supervision of Andreas Mielke. Research on simulating starling flocks with classical spin-glass models.
Publications
- S. Czischek, M. S. Moss, M. Radzihovsky, E. Merali, and R. G. Melko Data-enhanced variational Monte Carlo simulations for Rydberg atom arrays Phys. Rev. B 105, 205108 (2022)
- S. Czischek, M. Oberthaler, M. A. Petrovici, T. Gasenzer, M. Gärttner, et al. Spiking neuromorphic chip learns entangled quantum states SciPost Phys. 12, 039 (2022)
- S. Czischek, G. Torlai, S. Ray, R. Islam, and R. G. Melko Simulating a measurement-induced phase transition for trapped-ion circuits Phys. Rev. A 104, 062405 (2021)
- S. Czischek, M. Pioro-Ladrière, D. Drouin, R. G. Melko, et al. Miniaturizing neural networks for charge state autotuning in quantum dots Mach. Learn.: Sci. Technol. 3, 015001 (2021)
- M. Neugebauer, L. Fischer, A. Jäger, S. Czischek, S. Jochim, M. Weidemüller, and M. Gärttner Neural-network quantum state tomography in a two-qubit experiment Phys. Rev. A 102, 042604 (2020)
- S. Czischek Neural-Network Simulation of Strongly Correlated Quantum Systems Springer Theses, Springer Nature (2020)
- S. Czischek, J. M. Pawlowski, T. Gasenzer, and M. Gärttner Sampling scheme for neuromorphic simulation of entangled quantum systems Phys. Rev. B 100 195120 (2019)
- S. Czischek, M. Gärttner, M. Oberthaler, M. Kastner, T. Gasenzer Quenches near criticality of the quantum Ising chain—power and limitations of the discrete truncated Wigner approximation Quantum Sci. Technol. 4, 014006 (2019)
- S. Czischek, M. Gärttner, T. Gasenzer Quenches near Ising quantum criticality as a challenge for artificial neural networks Phys. Rev. B 98, 024311 (2018)