M
Masayuki Ohzeki
Researcher at Tohoku University
Publications - 171
Citations - 2209
Masayuki Ohzeki is an academic researcher from Tohoku University. The author has contributed to research in topics: Quantum annealing & Ising model. The author has an hindex of 21, co-authored 156 publications receiving 1563 citations. Previous affiliations of Masayuki Ohzeki include Kyoto University & Sapienza University of Rome.
Papers
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Journal ArticleDOI
Quantum Speed Limit is Not Quantum.
Manaka Okuyama,Masayuki Ohzeki +1 more
TL;DR: This study obtains a classical speed limit corresponding to the QSL using Hilbert space for the classical Liouville equation and obtains similar speed limits for the imaginary-time Schrödinger equations such as the classical master equation.
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Strong resilience of topological codes to depolarization
Hector Bombin,Ruben S. Andrist,Masayuki Ohzeki,Masayuki Ohzeki,Helmut G. Katzgraber,Helmut G. Katzgraber,Miguel Angel Martin-Delgado +6 more
TL;DR: In this paper, the authors compute the stability of the toric code to depolarization by mapping the quantum problem onto a classical disordered eight-vertex Ising model, and demonstrate an increased error threshold of 18.9(3)% when noise correlations are taken into account.
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Compressing Green's function using intermediate representation between imaginary-time and real-frequency domains
TL;DR: In this paper, a model-independent compact representation of imaginary-time data is presented in terms of the intermediate representation (IR) of analytical continuation, and the efficiency of the IR through continuous-time quantum Monte Carlo calculations of an Anderson impurity model is demonstrated.
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Strong Resilience of Topological Codes to Depolarization
Hector Bombin,Ruben S. Andrist,Masayuki Ohzeki,Masayuki Ohzeki,Helmut G. Katzgraber,Helmut G. Katzgraber,Miguel Angel Martin-Delgado +6 more
TL;DR: In this paper, the authors compute the stability of the toric code to depolarization by mapping the quantum problem onto a classical disordered eight-vertex Ising model, and demonstrate an increased error threshold of 18.9(3)% when noise correlations are taken into account.
Journal ArticleDOI
Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data.
TL;DR: A data-science approach to solving the ill-conditioned inverse problem for analytical continuation by means of a modern regularization technique, which eliminates redundant degrees of freedom that essentially carry the noise, leaving only relevant information unaffected by the noise.