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954 results for “quantum”
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Supporting data for "Measurable fractional spin for quantum Hall quasiparticles on the disk"
<p>Supporting data for the manuscript "Measurable fractional spin for quantum Hall quasiparticles on the disk", by T. Comparin, A. Opler, E. Macaluso, A. Biella, A. P. Polychronakos, L. Mazza.<br> If you use these numerical results in a scientific work, please cite the corresponding article [<a href="https://link.aps.org/doi/10.1103/PhysRevB.105.085125">Phys. Rev. B <strong>105</strong>, 085125 (2022)</a>].<br> For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr).</p> <p>We computed the density profile rho(r) for the Laughlin state (with filling 1/m, for m=2,3,4) and for the Halperin 221 state, by means of Monte Carlo simulations. All data are in units of the magnetic length (that is, with lB=1).<br> When present, labels "QH0", "QH1" and "QH2" in the filenames correspond to the case with q=0, q=1 or q=2 quasiholes localized at the origin.</p> <p><br> Folders:</p> <ul> <li>Data_Laughlin contains the Laughlin density-profile data used to compute the spin values in Fig. 1.</li> <li>Data_Halperin221 contains the Halperin 221 density-profile data used to compute the spin values in Fig. 2. Filenames include a label for the type and number of quasiholes: "A" stands for Gamma=A and q=1; "AA" stands for Gamma=A and q=2; "AB" stands for Gamma=AB and q=1; "AABB" stands for Gamma=AB and q=2.</li> <li>Data_Laughlin_boundary contains the Laughlin density-profile data shown in Fig. 6.</li> <li>Data_Halperin221_boundary_A contains the Laughlin density-profile data shown in Fig. 7, for Gamma=A.</li> <li>Data_Halperin221_boundary_AB contains the Laughlin density-profile data shown in Fig. 7, for Gamma=AB.</li> </ul>
Deliverable D2 (D1.4) - Functional quantum device grown with FEBID
<p>Superconductors are one of the building blocks for most nanodevices used in quantum applications. The direct local writing aspect of the Focused Electron Beam Induced Deposition (FEBID) technique allows for flexible deposition of any materials and especially metals and superconductors. As clean bulk material tungsten (W) has a superconducting transition temperature Tc = 15 mK only and a critical field Bc = 0.12 mT. Tc can be increased by introducing disorder and carbon incorporation to about 5.5 K and Bc to 9.5 T using e.g. ion- induced deposition techniques [3, 4]. Superconducting tungsten fabricated by FEBID has been so far reported only twice in the literature [1, 2]. Here we report the use of tungstenhexacarbonyl W(CO)6 molecules as a precursor for fabricating superconducting tungsten by FEBID.</p>
Quantum Monte Carlo data for 4He inside Ar-Preplated MCM-41 Nanopores
<p>Raw quantum Monte Carlo data for argon pre-plated MCM-41 nanopores with L = 50 Å.</p> <p>Details of the simulations are published here: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.144505</p>
Data Analysis files for "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity"
<p>Data Analysis for the paper "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity", which is published in PRX Quantum <strong>3</strong>, 020309 (2022).</p>
Raw data to "Scaling at quantum phase transitions above the upper critical dimension"
<p>This directory contains the data used to generate the numerical results in the work "Scaling at quantum phase transitions above the upper critical dimension[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: A. Langheld et al., Scaling at quantum phase transitions above the upper critical dimension, <a href="https://scipost.org/10.21468/SciPostPhys.13.4.088">SciPost Phys. <strong>13</strong> 088</a>, 2022.</p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset
<p>Simulated sydromes measurement of quantum surface code error correction.<br>Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>".</p> <p>File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code></p> <p>Each file is formatted as csv with the following columns:</p> <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> <p>Only distance 3 is currently available with 10M samples for each physical fault rate.</p> <p>The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data
<p>Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"</p>
Numerical data for "Nonlocal correlations transmitted between quantum dots via short topological superconductor"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Nonlocal correlations transmitted between quantum dots </em><em>via short topological superconductor</em> (arXive preprint <a href="http://arxiv.org/abs/2405.06630">http://arxiv.org/abs/2405.06630</a>), and other data obtained within the same project. See file headers for details concerning the content.</p>
Data for "Optimization of decoder priors for accurate quantum error correction"
<p>Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.</p>
Supplementary Information and Data for "Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots"
<p>Raw and processed TEM and AFM data for the article <strong><em>Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots</em></strong>.</p> <p>Paper: <a href="https://doi.org/10.1021/acs.nanolett.4c02182" target="_blank" rel="noopener">https://doi.org/10.1021/acs.nanolett.4c02182</a></p> <p>Preprint: <a href="https://arxiv.org/abs/2405.16073" target="_blank" rel="noopener">https://arxiv.org/abs/2405.16073</a></p> <p>The TEM data has a PDF information file included with description of the file types and how to open them.</p> <p>The AFM Nanosurf .nid files can be opened, e.g., with <a href="http://gwyddion.net/" target="_blank" rel="noopener">Gwyddion</a>.</p>
Data and codes for Orozco-Ruiz et al. "Quantum control without quantum states"
<p>Simulation data and analytic structure constants for the paper "Quantum control without quantum states" by Modesto Orozco-Ruiz, Nguyen Le, and Florian Mintert.</p>
Supporting data for "A 2x2 quantum dot array with controllable inter-dot tunnel couplings"
<p>Supporting data and analysis scripts for all figures in in "A 2x2 quantum dot array with controllable inter-dot tunnel couplings", arXiv:1802.05446 (preprint)</p> <p>This dataset contains a readme file, a data file in hdf5 format containing all the relevant datasets, and a python script file that can be used to access, analyse and plot the datasets in the data file, reproducing the plots in the figures presented in the manuscript.</p>
Data of publication Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies
<p>Data corresponding to the figures of the publication " Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies" by S. Liu et al. (https://pubs.rsc.org/en/content/articlelanding/2018/ra/c8ra07246a#!divAbstract). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Data-driven criteria for quantum correlations
<p>Dataset of 3-qubit density matrices used for training deep-learning models for quantum correlations identicfication introduced in two papers:</p> <ul> <li> <div>M Krawczyk, J Pawłowski, MM Maśka, K Roszak Physical Review A 109, 022405 (2024).</div> <div> </div> </li> <li> <div>J Pawłowski, M Krawczyk, Physical Review Applied 22, 014068 (2024).</div> <div> </div> </li> </ul>
Data for "Quantum error correction below the surface code threshold"
<p>Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor.</p> <p>See README at the root of each zip archive for detailed description of each dataset.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.