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Matthias Troyer

Researcher at Microsoft

Publications -  481
Citations -  35590

Matthias Troyer is an academic researcher from Microsoft. The author has contributed to research in topics: Quantum Monte Carlo & Monte Carlo method. The author has an hindex of 86, co-authored 473 publications receiving 28965 citations. Previous affiliations of Matthias Troyer include University of Zurich & ETH Zurich.

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Journal ArticleDOI

Mutual information in classical spin models

TL;DR: In this paper, the total many-body correlations present in finite temperature classical spin systems were studied using the concept of mutual information, and the Shannon mutual information and the Renyi mutual information in both Ising and Potts models in two dimensions were calculated numerically by combining matrix product state algorithms and Monte Carlo sampling techniques.
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Phase diagram of Bose-Fermi mixtures in one-dimensional optical lattices.

TL;DR: In this paper, the ground state phase diagram of the one-dimensional Bose-Fermi Hubbard model is studied in the canonical ensemble using a quantum Monte Carlo method, focusing on the case where both species have half filling in order to maximize the pairing correlations between the bosons and the fermions.
Journal ArticleDOI

The Phase Diagram of the $\nu=5/2$ Fractional Quantum Hall Effect: Effects of Landau Level Mixing and Non-Zero Width

TL;DR: In this paper, the authors numerically diagonalize a comprehensive effective Hamiltonian describing the fractional quantum Hall effect of electrons under realistic conditions in GaAs semiconductors, and find the ground state in both the torus and spherical geometries as a function of the quantum well and sub-band mixing.
Journal ArticleDOI

Ferromagnetism of the one-dimensional Kondo-lattice model: A quantum Monte Carlo study.

TL;DR: The one-dimensional Kondo-lattice model is investigated using quantum Monte Carlo and transfer-matrix techniques and shows a Ruderman-Kittel-Kasuya-Yosida-like behavior in the weak-coupling region.
Book ChapterDOI

Bridging workflow and data provenance using strong links

TL;DR: A persistent storage mechanism is proposed that manages input, intermediate, and output data files, strengthening the links between provenance and data and provides better support for reproducibility.