Sparse modeling in quantum many-body problems
This review paper describes the basic concept and technical details of sparse modeling and
its applications to quantum many-body problems. Sparse modeling refers to methodologies …
its applications to quantum many-body problems. Sparse modeling refers to methodologies …
Binary optimization by momentum annealing
T Okuyama, T Sonobe, K Kawarabayashi, M Yamaoka - Physical Review E, 2019 - APS
One of the vital roles of computing is to solve large-scale combinatorial optimization
problems in a short time. In recent years, methods have been proposed that map …
problems in a short time. In recent years, methods have been proposed that map …
Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
A data-science approach to solving the ill-conditioned inverse problem for analytical
continuation is proposed. The root of the problem lies in the fact that even tiny noise of …
continuation is proposed. The root of the problem lies in the fact that even tiny noise of …
SpM: Sparse modeling tool for analytic continuation of imaginary-time Green's function
We present SpM, a sparse modeling tool for the analytic continuation of imaginary-time
Green's function, licensed under GNU General Public License version 3. In quantum Monte …
Green's function, licensed under GNU General Public License version 3. In quantum Monte …
Computing architecture to perform approximated simulated annealing for Ising models
T Okuyama, C Yoshimura, M Hayashi… - 2016 IEEE …, 2016 - ieeexplore.ieee.org
In the near future, the techniques to solve combinatorial optimization problems will become
important in various fields and require large computing power. However, the performance …
important in various fields and require large computing power. However, the performance …
Statistical-mechanical analysis of compressed sensing for hamiltonian estimation of ising spin glass
Several powerful machines, such as the D-Wave 2000Q, dedicated to solving combinatorial
optimization problems through the Ising-model formulation have been developed. To input …
optimization problems through the Ising-model formulation have been developed. To input …
Inference of the sparse kinetic Ising model using the decimation method
In this paper we study the inference of the kinetic Ising model on sparse graphs by the
decimation method. The decimation method, which was first proposed in Decelle and Ricci …
decimation method. The decimation method, which was first proposed in Decelle and Ricci …
Quantum annealing: next-generation computation and how to implement it when information is missing
Recently, several powerful machines dedicated to solving combinatorial optimization
problems through the Ising-model formulation have appeared. The trigger for the paradigm …
problems through the Ising-model formulation have appeared. The trigger for the paradigm …
Phase Transition in Binary Compressed Sensing Based on L1-norm Minimization
Compressed sensing is a signal processing scheme that reconstructs high-dimensional
sparse signals from a limited number of observations. In recent years, various problems …
sparse signals from a limited number of observations. In recent years, various problems …
Phase transition in binary compressed sensing based on -norm minimization
M Ohzeki - arXiv preprint arXiv:2405.16824, 2024 - arxiv.org
Compressed sensing is a signal processing scheme that reconstructs high-dimensional
sparse signals from a limited number of observations. In recent years, various problems …
sparse signals from a limited number of observations. In recent years, various problems …