1 code implementation • 27 Apr 2023 • Ryo Tamura, Koji Tsuda, Shoichi Matsuda
Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system.
no code implementations • 1 Sep 2022 • Yuya Seki, Ryo Tamura, Shu Tanaka
Ising machines are useful for binary optimization problems because variables can be represented by a single binary variable of Ising machines.
no code implementations • 30 Apr 2021 • Syun Izawa, Koki Kitai, Shu Tanaka, Ryo Tamura, Koji Tsuda
As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back.
no code implementations • 13 Jun 2020 • Ryo Tamura, Koji Hukushima, Akira Matsuo, Koichi Kindo, Masashi Hase
We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty, from multiple physical quantities.
1 code implementation • 25 Oct 2019 • Xiaolin Sun, Zhufeng Hou, Masato Sumita, Shinsuke Ishihara, Ryo Tamura, Koji Tsuda
Machine learning applications in materials science are often hampered by shortage of experimental data.
1 code implementation • 6 Dec 2018 • Kei Terayama, Ryo Tamura, Yoshitaro Nose, Hidenori Hiramatsu, Hideo Hosono, Yasushi Okuno, Koji Tsuda
Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient.
Materials Science Computational Physics