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?
- 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]