Data for: Enhanced Krylov Methods for Molecular Hamiltonians via Tensor Hypercontraction

Zenodo2025-01-28 更新2026-04-07 收录

分子哈密顿量学习

张量网络方法

数据链接：

https://zenodo.org/doi/10.5281/zenodo.14752266 数据链接链接失效反馈

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资源简介：

Data is generated by codes based on PyTeNet https://github.com/cmendl/pytenet/tree/master

创建时间：

2025-01-28

Scalably learning quantum many-body Hamiltonians from dynamical data

分子哈密顿量学习

量子系统表征

Our paper on Hamiltonian learning for large quantum systems contains several numerical results. The results were produced with the differentiable-tebd package which we developed for this study. The sc

NIAID Data Ecosystem50

TNSP: A framework supporting symmetry and fermion tensors for tensor network state methods

多体系统模拟

张量网络方法

Recent advancements have established tensor network states (TNS) as formidable tools for exploring the complex realm of strongly-correlated many-particle systems in both one and two dimensions. To tac

NIAID Data Ecosystem10

Scalably learning quantum many-body Hamiltonians from dynamical data

分子哈密顿量学习

多体系统模拟

Zenodo2022-11-17 更新00

Open source Matrix Product States: Opening ways to simulate entangled many-body quantum systems in one dimension

一维量子多体模拟

张量网络方法

Numerical simulations are a powerful tool to study quantum systems beyond exactly solvable systems lacking an analytic expression. For one-dimensional entangled quantum systems, tensor network methods

NIAID Data Ecosystem30

Data for "FC2DES: Modeling 2D electronic spectroscopy for the Generalized Brownian Oscillator Molecular Hamiltonian"

二维电子光谱建模

分子哈密顿量学习

Within is the 2DES presented in the manuscript: "FC2DES: Modeling 2D electronic spectroscopy for the Generalized Brownian Oscillator Molecular Hamiltonian" as well as the parameter and input files use

Zenodo2024-11-19 更新10

Data for: Enhanced Krylov Methods for Molecular Hamiltonians via Tensor Hypercontraction

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