## research interests

My research interests lies somewhere in the intersection of quantum physics and what has been coined “data science” in recent years. Some questions I am trying to answer currently include

- Can we adapt techniques from low-rank matrix recovery to the characterize quantum technological devices?
- What constitutes a “good” error region for quantum state estimation? Are there computational limits on how we can incorporate prior information to improve an error region?
- How can we circumvent the “curse of dimensionality” in quantum state estimation by exploiting prior information on the physical system?
- How does the quantum-inspired matrix product (or tensor train) tensor decomposition help us to solve real-world data problems more efficiently?

I am a strong proponent of open science. This is why you can find most of my work on github. There, you can also find slides and posters of previous presentations.

## publications

- Z. Stojanac, D. Suess, M. Kliesch:
*On the distribution of a product of N Gaussian random variables*, Proceedings Volume 10394, Wavelets and Sparsity XVII; 1039419 (2017) - D. Suess, L. Rudnicki, D. Gross:
*Error regions in quantum state tomography: computational complexity caused by geometry of quantum states*, New J. Phys. 19 093013 (2017) [arXiv:1608.00374] - D. Suess, W. T. Strunz, A. Eisfeld:
*Hierarchical equations for open system dynamics in fermionic and bosonic environments*, J. Stat. Phys. 159, Issue 6, pp 1048–1423 (2015) [arXiv:1410.0304] - G. Ritschel, D. Suess W. T. Strunz, A. Eisfeld:
*Non-Markovian Quantum State Diffusion for temperature-dependent linear spectra of light harvesting aggregates*, J. Chem. Phys. 142, 034115 (2015) [arXiv:1409.1091] - D. Suess, A. Eisfeld, W. T. Strunz:
*Hierarchy of stochastic pure states for open quantum system dynamics*, Phys. Rev. Lett. 113, 150403 (2014) [arXiv:1402.4647]