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pro vyhledávání: '"Yoshida, Sota"'
Developing methods to solve nuclear many-body problems with quantum computers is an imperative pursuit within the nuclear physics community. Here, we introduce a quantum algorithm to accurately and precisely compute the ground state of valence two-ne
Externí odkaz:
http://arxiv.org/abs/2404.01694
Autor:
Yoshida, Sota
We present a novel method, IMSRG-Net, which utilizes machine learning techniques as a solver for the in-medium Similarity Renormalization Group (IMSRG). The primary objective of IMSRG-Net is to approximate the Magnus operators $\Omega(s)$ in the IMSR
Externí odkaz:
http://arxiv.org/abs/2306.08878
Autor:
Yoshida, Sota
In the past decades, it has been shown that the three-body force is necessary for a fully microscopic understanding of nuclear many-body systems, and thus efficient schemes for storing and utilizing three-body matrix elements have been developed. How
Externí odkaz:
http://arxiv.org/abs/2208.02464
Associated to a knot diagram, Goeritz introduced an integral matrix, which is now called a Goeritz matrix. It was shown by Traldi that the solution space of the equations with Goeritz matrix (precisely, unreduced Goeritz matrix called in his paper) a
Externí odkaz:
http://arxiv.org/abs/2206.01983
Autor:
Yoshida, Sota, Shimizu, Noritaka
Publikováno v:
Prog Theor Exp Phys (2022)
Shell-model calculations play a key role in elucidating various properties of nuclei. In general, those studies require a huge number of calculations to be repeated for parameter calibration and quantifying uncertainties. To reduce the computational
Externí odkaz:
http://arxiv.org/abs/2105.08256
Autor:
Yoshida, Sota
Publikováno v:
Phys. Rev. C 102, 024305 (2020)
We propose a non-parametric extrapolation method based on constrained Gaussian processes for configuration interaction methods. Our method has many advantages: (i) applicability to small data sets such as results of {\it ab initio} methods, (ii) flex
Externí odkaz:
http://arxiv.org/abs/1907.04974
Publikováno v:
In Biosensors and Bioelectronics 15 January 2023 220
Publikováno v:
Phys. Rev. C 98, 061301 (2018)
The uncertainty quantifications of theoretical results are of great importance to make meaningful comparisons of those results with experimental data and to make predictions in experimentally unknown regions. By quantifying uncertainties, one can mak
Externí odkaz:
http://arxiv.org/abs/1810.03263
Akademický článek
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Autor:
Yoshida, Sota R.
Computer vision is the science and technology of machines that see, where seeing in this case means that the machine is able to extract information from an image that is necessary to solve some task. This new book presents topical research in the stu