M
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 Article
Understanding Quantum Tunneling through Quantum Monte Carlo Simulations
Sergio Boixo,Sergei V. Isakov,Guglielmo Mazzola,Vadim Smelyanskiy,Zhang Jiang,Hartmut Neven,Matthias Troyer +6 more
TL;DR: Performing quantum Monte Carlo (QMC) simulations, it is found that the QMC tunneling rate displays the same scaling with system size, as the rate of incoherent tunneling, and the scaling in both cases is O(Δ^{2}).
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Critical Exponents of the Quantum Phase Transition in a Planar Antiferromagnet
TL;DR: In this article, a large scale quantum Monte Carlo study of the quantum phase transition in a planar spin-1/2 Heisenberg antiferromagnet with CaV 4 O 9 structure was performed.
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Fidelity Susceptibility Made Simple: A Unified Quantum Monte Carlo Approach
TL;DR: In this paper, a generic tool was proposed to capture quantum phase transitions in a simple and efficient manner, which can be thought as certain classical phase transition in the modern formulation of quantum Monte Carlo methods.
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Performance analysis of continuous-time solvers for quantum impurity models
TL;DR: In this paper, a new class of continuous-time impurity solvers has been developed based on a diagrammatic expansion of the partition function in either the interactions or the impurity-bath hybridization.
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Estimating errors reliably in Monte Carlo simulations of the Ehrenfest model
Vinay Ambegaokar,Matthias Troyer +1 more
TL;DR: In this paper, the authors use the Ehrenfest urn model to illustrate the subtleties of error estimation in Monte Carlo simulations and show how the smooth results of correlated sampling in Markov chains can fool one's perception of the accuracy of the data and how to obtain reliable error estimates from correlated samples.