Tensor networks
Tensor networks develop analytical and numerical methods for the classical description of complex quantum many-body systems. Our research advances tensor network techniques, including matrix product states and projected entangled pair states for two-dimensional systems, to study strongly correlated quantum matter and its dynamics. We also develop efficient methods for the classical simulation of noisy quantum circuits, with the goal of identifying when quantum computations can be simulated efficiently on classical computers and thereby sharpening the boundary between classical and genuinely quantum computational advantages.
Selected recent group publications
- Scalably learning quantum many-body Hamiltonians from dynamical data
Quantum Science and Technology 11, 035002 (2026) - Large-scale stochastic simulation of open quantum systems
Nature Communications 16, 11074 (2025) - Quantum circuit simulation with a local time-dependent variational principle
arXiv:2508.10096 (2025) - Variationally optimizing infinite projected entangled-pair states at large bond dimensions: A split corner transfer matrix renormalization group approach
Physical Review B 111, 235116 (2025) - Unraveling long-time quantum dynamics using flow equations
Nature Physics 20, 1401 (2024) - Entanglement estimation in tensor network states via sampling
PRX Quantum 3, 030312 (2022) - Towards topological fixed-point models beyond gappable boundaries
Physical Review B 106, 125143 (2022) - Tensor network investigation of the double layer Kagome compound Ca10Cr7O28
Annals of Physics 421, 168292 (2020) - A tensor network annealing algorithm for two-dimensional thermal states
Physical Review Letters 122, 070502 (2019)
Group reviews
- An introduction to infinite PEPS methods for variational ground state simulations using automatic differentiation
SciPost Physics Lecture Notes 86 (2024) - Entanglement and tensor network states
Modelling and Simulation 3, 520 (2013) - Area laws for the entanglement entropy
Reviews of Modern Physics 82, 277 (2010)
