Roeland Wiersema

About me

I am a researcher at the Center for Computational Quantum Physics in the Flatiron Institute in New York.


I grew up in the Netherlands, in the city of Nijmegen. I did my undergraduate studies at Radboud University, where I pursued a bachelor in Physics and Astronomy and a master's in Particle and Astrophysics. Under the supervision of Bert Kappen, I wrote a master's thesis on quantum-inspired machine learning. During my master's I founded a machine learning consultancy company with three of my friends. After completing some projects we decided to pursue different directions with some of use leaving for academia and some headed to industry. I decided on academia, and started my PhD at the University of Waterloo and the Vector Institute in 2020. I worked on variational quantum computing and machine learning for quantum many-body physics under the supervision of Juan Carrasquilla and Roger Melko. In 2024, I defended my PhD after writing a thesis titled Quantum Computing: Optimization and Geometry. During my PhD, I worked with Xanadu Quantum Technologies on several projects, and did an internship at Los Alamos National Laboratory.

Research Interests

My research is focused on computational methods for quantum many-body physics. Some of the methods that I used in my research are Quantum Computing, Tensor Networks and Neural Quantum States.

Neural Quantum States for dynamics

Related works:
[1] King, Andrew D.,...,Wiersema et al. Computational supremacy in quantum simulation. arXiv preprint arXiv:2403.00910, 2024.

Differential geometry approaches in variational quantum computing

Related works:
[1] Wiersema et al., Geometric Quantum Machine Learning with Horizontal Quantum Gates. (inpreparation), 2024
[2] Wiersema et al., Classification of dynamical Lie algebras for translation-invariant 2-local spin systems in one dimension. arXiv:2203.05690, 9 2023
[3] Kokcu, Wiersema et al., Classification of dynamical Lie algebras generated by spin systems on undirected graphs. (in preparation), 2024
[4] Wiersema et al., Here comes the SU(N): multivariate quantum gates and gradients. Quantum, 8:1275, March 2024
[5] Wiersema et al., Optimizing quantum circuits with Riemannian gradient flow. Phys. Rev. A, 107(6):062421, Jun 2023

Circuit Compilation with Tensor Networks