The University of Tokyo · 물리·천문학
유이치 모โต야마 교수의 연구실은 양자 many-body 시스템의 수치적 해석과 고도로 복잡한 양자 상의 특성 분석을 핵심으로 합니다. 주로 토너스 네트워크 기반의 수치 계산 방법(예: TeNeS 소프트웨어 개발)과 양자 위상 및 위상적 순서를 기반으로 한 스핀계의 대칭 보호(topological) 상 분류를 연구합니다. 특히 Z(N) Berry 위상, 메론 클러스터 알고리즘, 그리고 실험 데이터 분석을 위한 오픈소스 소프트웨어(2DMAT) 개발을 통해 이론·계산·실험의 융합 연구를 선도하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
We present a loop cluster algorithm Monte Carlo method for calculating the local Z(2) Berry phase of the quantum spin models. The Berry connection, which is given as the inner product of two ground states with different local twist angles, is expressed as a Monte Carlo average on the worldlines with fixed spin configurations at the imaginary-time boundaries. The "complex weight problem" caused by the local twist is solved by adopting the meron cluster algorithm. We present the results of simulat
TeNeS (Tensor Network Solver) [1], [2] is a free/libre open-source software program package for calculating two-dimensional many-body quantum states based on the tensor network method and the corner transfer matrix renormalization group (CTMRG) method. This package calculates ground-state wavefunctions for user-defined Hamiltonians and evaluates user-defined physical quantities such as magnetization and correlation functions. For certain predefined models and lattices, there is a tool that makes
The local ${Z}_{N}$ quantized Berry phase for the $\mathrm{SU}(N)$ antiferromagnetic Heisenberg spin model is formulated. This quantity, which is a generalization of the local ${Z}_{2}$ Berry phase for SU(2) symmetry, has a direct correspondence to the number of singlet pairs spanning a particular bond, and is effective as a tool to characterize and classify the various symmetry-protected topological phases of one-dimensional $\mathrm{SU}(N)$ spin systems. We extend the path-integral quantum Mon
In our current era, numerical simulations have become indispensable theoretical and experimental tools for use in daily research activities, particularly in the materials science fields. However, the installation processes for such simulations frequently become problematic because they depend strongly on the device environment, and troubleshooting those processes is a challenging task for beginners. To minimize such difficulties, we created MateriApps LIVE! and MateriApps Installer, which can so
Quantum many-body systems are challenging targets for computational physics due to their large number of degrees of freedom. The tensor networks, particularly Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), effectively represent these systems on two-dimensional lattices. However, the technical complexity of TPS/PEPS-based coding can be challenging for many researchers to manage effectively. To reduce this problem, we developed TeNeS (Tensor Network Solver). This paper int
Quantum many-body systems are challenging targets for computational physics due to their large degrees of freedom. The tensor networks, particularly Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), effectively represent these systems on two-dimensional lattices. However, the technical complexity of TPS/PEPS-based coding is often too much for researchers to handle. To reduce this problem, we developed TeNeS (Tensor Network Solver). This paper introduces TeNeS-v2, which exte
An open-source data-analysis framework 2DMAT has been developed for experimental measurements of two-dimensional material structures. 2DMAT offers five analysis methods: (i) Nelder-Mead optimization, (ii) grid search, (iii) Bayesian optimization, (iv) replica exchange Monte Carlo method, and (v) population-annealing Monte Carlo method. Methods (ii) through (v) are implemented by parallel computation, which is efficient not only for personal computers but also for supercomputers. The current vers
Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While many BO frameworks focus on algorithmic efficiency, practical usability and portability are equally critical for sustained use in real research environments. PHYSBO is a Bayesian optimization library designed to address these needs by enabling optimization over user-defined candidate pools and by supporting domain-spec
In our current era, numerical simulations have become indispensable theoretical and experimental tools for use in daily research activities, particularly in the materials science fields. However, the installation processes for such simulations frequently become problematic because they depend strongly on the device environment, and troubleshooting those processes is a challenging task for beginners. To minimize such difficulties, we created MateriApps LIVE! and MateriApps Installer, which can so
Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While many BO frameworks focus on algorithmic efficiency, practical usability and portability are equally critical for sustained use in real research environments. PHYSBO is a Bayesian optimization library designed to address these needs by enabling optimization over user-defined candidate pools and by supporting domain-spec
