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Data Files for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)

Peizhi Mai, Benjamin Cohen-Stead, Thomas Maier, Steven Johnston

Published Sep 5, 2024• 1 citation
Publisher

Abstract

These are the data for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024) arXiv reference: https://arxiv.org/abs/2405.13164 This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.

DOI
10.5281/zenodo.13693044

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These are the data for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024) arXiv reference: https://arxiv.org/abs/2405.13164 This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.
Data and code to reproduce the "Freezing and Shielding under Global Quenches for Many-Body Systems with Long-Range Couplings" data in https://arxiv.org/abs/2407.06072. The mathematica file just perform a numerical integral that computes the double occupaciens with second order unitary perturbation theory for a semi-circular density of states of the unperturbed state. The python files also compute double occupaciens with second order unitary perturbation theory for a finite system where the density of states cannot be approximated by a continuous functions but it is just a collection of eigenenergies. The Fig1.ipynb file computes the dynamics of three local excitations for the spin model and the Fermi-Hubbard model with off-diagonal disorder and long-range hopping/interactions. Ther is a python file that computes the fidelity decay.
Data from the simulations presented in arXiv:2104.00569, and generated with the code published in https://github.com/matteoacrossi/adapt_ic-povm # Instructions The file data.tar contains the following files: hamiltonians_h_chain.pickle hamiltonians_params.json hamiltonians.pickle chemistry.feather h_chain.feather tomography_counts_data.jsonl fidelities_tomo.json The files `hamiltonians*` contain the Hamiltonians considered in the paper, as well as the corresponding exact ground states and example VQE ground states. The files `chemistry.feather` and `h_chain.feather` contain all the results for the molecules and hydrogen chain (respectively). The files `tomography_counts_data.jsonl` and `fidelities_tomo.json` contain (respectively) the raw data for tomographical reconstruction and the fidelities of the reconstructed density matrices used to produce Fig. 5. The files can be processed using the Figures.ipynb file in the linked repository. Please refer to the README.md file for more detail on how to use and reproduce this data
This repository contains the numerical data and computational codes supporting the results presented in the manuscript: "Engineered non-Gaussian Coherence as a Thermodynamic Resource for Quantum Batteries". The archive (data_QB.zip) includes all datasets, Jupyter notebooks, and figure files required to reproduce the results reported in both the main text and the Supplemental Material. Each figure is organised in a separate directory containing the corresponding data (.txt), code (.ipynb), and generated plots (.png/.pdf). The provided materials are sufficient to reproduce all figures and results without restriction. Detailed instructions and directory structure are described in the accompanying README.txt file. This dataset is made openly available to ensure transparency and reproducibility of the reported results.
The HHL algorithm is a prominent quantum algorithm that offers exponential speedup over its classical counterparts for solving a system of linear equations. However, synthesizing and executing HHL circuits demand significant computational resources from both classical and quantum systems. In this paper, we benchmark the HHL algorithm using the optimized Quantum Phase Estimation (QPE) generation algorithm, LuGo [1], to enhance its scalability and efficiency. We leverage various highperformance computing (HPC) systems–the National Energy Research Scientific Computing Center's (NERSC's) Perlmutter supercomputer and Oak Ridge Leadership Computing Facility's (OLCF's) Andes system and Frontier supercomputer–to evaluate the scalability of generating HHL circuits and to measure the time to simulate the generated circuits. Additionally, enabled by the Quantum Computing User Program (QCUP) of OLCF, we provide a comprehensive analysis of the algorithm's performance on various state-of-the-art superconducting and trappedion quantum devices, including studies on qubit connectivity, fidelity comparisons, and hardware compatibility and robustness. Our results offer preliminary insights into potential practical applications of the HHL algorithm enabled by LuGo and the performance of various types of quantum hardware.11Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.