activity
20202026
most citedDeterminant-free fermionic wave function using feed-forward neural networks

34 citations · 92 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2026

A Differentiable DFT-Based Framework for Inverse Materials Design

Kohei Ishii, Hisazumi Akai, Tetsuya Fukushima +2

Discovering solid-state materials with target properties remains a central challenge in computational materials science. Existing approaches -- high-throughput screening, surrogate…

cond-mat.mtrl-sci2024★ 1 cited

Inverse magneto-conductance design by automatic differentiation

Yuta Hirasaki, Koji Inui, Eiji Saitoh

Magneto-conductance in thin wires often exhibits complicated patterns due to the quantum interference of conduction electrons. These patterns reflect microscopic structures in the…

cond-mat.str-el2024★ 2 cited

Inverse Hamiltonian design of highly-entangled quantum systems

Koji Inui, Yukitoshi Motome

Solving inverse problems to identify Hamiltonians with desired properties holds promise for the discovery of fundamental principles. In quantum systems, quantum entanglement plays…

quant-ph2023★ 24 cited

A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?

Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui +18

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring…

cond-mat.str-el2022★ 13 cited

Inverse Hamiltonian design by automatic differentiation

Koji Inui, Yukitoshi Motome

An ultimate goal of materials science is to deliver materials with desired properties at will. In the theoretical study, a standard approach consists of constructing a Hamiltonian…

cond-mat.str-el2021★ 34 cited

Determinant-free fermionic wave function using feed-forward neural networks

Koji Inui, Yasuyuki Kato, Yukitoshi Motome

We propose a general framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks. The anticommutation relation for fermions is usual…

cond-mat.str-el2020★ 18 cited

Channel-selective non-Fermi liquid behavior in the two-channel Kondo lattice model under a magnetic field

Koji Inui, Yukitoshi Motome

Stimulated by anomalous behaviors found in non-Kramers -electron systems in an applied magnetic field, we study a two-channel Kondo lattice model by using a cluster extension of…