activity
20162026
most citedDeep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions

69 citations · 98 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.dis-nn2026

Reservoir computing by thin film embedded with magnetic impurities

Shuto Kamakura, Tomi Ohtsuki, Jun-ichiro Ohe

The reservoir computing based on the thin film embedded with magnetic impurities in the presence of the long-range (the dipole-dipole) interaction is numerically investigated. We s…

quant-ph2025

Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning

Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi +3

Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits req…

cond-mat.stat-mech2024★ 9 cited

Universal Stochastic Equations of Monitored Quantum Dynamics

Zhenyu Xiao, Tomi Ohtsuki, Kohei Kawabata

We investigate the monitored quantum dynamics of Gaussian mixed states and derive the universal Fokker-Planck equations that govern the stochastic time evolution of entire density-…

cond-mat.mes-hall2024★ 2 cited

Negative longitudinal resistance of monolayer graphene in the quantum Hall regime

Alexey A. Kaverzin, Shunsuke Daimon, Takashi Kikkawa +2

In the quantum Hall regime the charge current is carried by ideal one-dimensional edge channels where the backscattering is prohibited by topology. This results in the constant pot…

quant-ph2024★ 14 cited

Forecasting long-time dynamics in quantum many-body systems by dynamic mode decomposition

Ryui Kaneko, Masatoshi Imada, Yoshiyuki Kabashima +1

Reliable numerical computation of quantum dynamics is a fundamental challenge when the long-ranged quantum entanglement plays essential roles as in the cases governed by quantum cr…

quant-ph2023★ 4 cited

Channel Attention for Quantum Convolutional Neural Networks

Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi +3

Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. Reduction in the cost of training as wel…

cond-mat.dis-nn2016★ 69 cited

Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions

Tomi Ohtsuki, Tomoki Ohtsuki

Three-dimensional random electron systems undergo quantum phase transitions and show rich phase diagrams. Examples of the phases are the band gap insulator, Anderson insulator, str…