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15 results for “quantum information”
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </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>
Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'
<p>Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.</p>
Data of the publication: Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing
<p>Data corresponding to the figures of the publication "Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing" by N. Kunkel and Ph. Goldner (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Analyzing variational quantum landscapes with information content
<p>This repository contains the code, data and notebooks to reproduce the figures of "Analyzing variational quantum landscapes with information content".</p>
Detecting and Tracking Drift in Quantum Information Processors
<p>This is supplemental data and code for:<strong> </strong>T. Proctor et al, <a href="https://www.nature.com/articles/s41467-020-19074-4"><em>Detecting and tracking drift in quantum information processors</em></a>, Nat. Comm. 11, 5396 (2020).</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does <em>not</em> work on the latest full release of pyGSTi (0.9.9). It is anticipated that it will work with the next full release of pyGSTi (0.9.10).</p> <p>Below is a basic guide to navigating this SI:</p> <p><strong>Time-resolved Ramsey tomography on experimental data.</strong></p> <p><em>Directory: ramsey/experiment</em></p> <p>This folder contains the data and analysis code for the time-resolved Ramsey experiment, the results of which are presented in Figure 1 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis.</p> <p><strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong></p> <p><em>Directory: rb/simulation</em></p> <p>This folder contains the data and analysis code for the simulation of time-resolved RB, the results of which are presented in Figure 2 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis on the simulated data, and which can be used to run new simulations with the same noise model.</p> <p><strong>Time-resolved gate set tomography (GST) on simulated data.</strong></p> <p><em>Directory: gst/simulation</em></p> <p>This contains the data and analysis code for the simulation of time-resolved GST, the results of which are presented in Figure 2 of the paper. The raw simulated data is contained in the "data" folder. All the code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>create_simulated_data.py : this generates the simulated data. This was run using MPI on 20 cores.</li> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul> <p><strong>Time-resolved gate set tomography (GST) on experimental data.</strong></p> <p><em>Directory: gst/experiments</em></p> <p>This folder contains the data and analysis code for the two time-resolved GST experiments, the results of which are presented in Figure 3 of the paper. The raw data is contained in the two folders "data/1" and "data/2", corresponding to the first and second experiment, respectively. All analysis code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul>
Raw Data to 'Experimental verification of the area law of mutual information in quantum field theory', arXiv:2206.10563
<p><strong>Absorption images representing the raw data for arXiv:2206.10563</strong></p> <p>'scan5722.zip' contains the raw data for figures 2 and 3.</p> <p>'scan5831.zip' and 'scan9617.zip' contain the raw data for figure 5, right and left, respectively.</p> <p>All three datasets, 9617, 5722, and 5831, are used to obtain the data points in figure 4, from low to high temperatures.</p> <p> </p> <p><strong>Scans 5722 & 5831</strong></p> <p>The absorption images are numbered consecutively.</p> <p>The first image for scans 5722 and 5831 is taken along the axial (longitudinal) direction with our 'longitudinal' imaging system after 10 ms time of flight (TOF) to measure the atom number balance between the two wells. The measurement is performed before ramping up the DW barrier. Two images are taken in each cycle: One shot with atoms ('1-atomcloud.tif') and a second image to record the intensity of the imaging beam without atoms ('1-withoutatoms.tif'). These two pictures are used to extract the atomic density (see the Matlab script).</p> <p>The second image for scans 5722 and 5831 is taken in the direction of the double-well (DW) separation with our 'transverse' imaging system. The measurement is performed before ramping up the DW barrier. The image is taken after 11.2 ms TOF.</p> <p>The subsequent images record the interference fringes for the different evolution times (again, always pairs '-atomcloud.tif' and '-withoutatoms.tif'). They are taken with our 'vertical' imaging system after 15.6 ms TOF. The imaging direction is perpendicular to the weakly confined direction of the clouds and the DW separation.</p> <p>The recorded evolution times for scan 5722 are -1.9 ms (right before ramping up the DW barrier), 0 ms (right after the DW barrier is ramped up), and then in steps of 2.5 ms until 65 ms. This means that '3-atomcloud.tif' corresponds to -1.9 ms, and '30-atomcloud.tif' corresponds to 65 ms. This completes the 'first repeat'. The next two shots, '31-atomcloud.tif' and '32-atomcloud.tif', belong to the 'second repeat' and are again taken with the 'longitudinal' & 'transverse' imaging systems, respectively. The picture '33-atomcloud.tif' is again taken with the 'vertical' imaging and corresponds to -1.9 ms. And so forth.</p> <p>In the same way, the pictures scan 5831 are ordered. The evolution times (in ms) for these two scans are:</p> <p>scan 5722: -1.9 from 0 to 65 (in steps of 2.5)</p> <p>scan 5831: -1.8, from 0 to 65 (in steps of 2.5)</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p> </p> <p><strong>Scan 9617</strong></p> <p>This scan only contains images with the 'vertical' imaging system with 15.6 ms TOF. The evolution times (in ms) are as follows:</p> <p>scan 9617: -2.8, from 0 to 15 (in steps of 1.5), 22, 25, 28</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p>This means, that '1-atomcloud.tif', '16-atomcloud.tif', '31-atomcloud.tif' and so on correspond to -2.8 ms, and '15-atomcloud.tif', '30-atomcloud.tif', '45-atomcloud.tif' and so on correspond to 28 ms.</p> <p> </p> <p><strong>Matlab script</strong></p> <p>In addition to the data, a Matlab script (calc_atomic_density.m) illustrates how to extract the two-dimensional atomic density from the absorption images. It contains all relevant parameters of the imaging systems.</p>
Data from: On-chip distribution of quantum information using traveling phonons
<p>Source data for Figures.</p>
Supporting Information for the Journal Article "Automated Construction of Quantum–Classical Hybrid Models"
<p>This dataset contains the supporting information published together with the article "Automated Construction of Quantum–Classical Hybrid Models" (<a href="https://doi.org/10.1021/acs.jctc.1c00178"><em>J. Chem. Theory Comput.</em>, <strong>2022</strong>, <em>17</em>, 3797</a>).</p>
Simultaneous transmission of information and key exchange using the same photonic quantum states
Open the record for dataset details and reuse information.
Efficiently characterizing quantum information flow, loss and recovery in the central spin system
Open the record for dataset details and reuse information.
Supplementary Information: On the Role of Dielectric Screening in Calculating Excited States of Solvated Azobenzene: A Benchmark Study Comparing Quantum Embedding and Polarizable Continuum Model for Representing the Solvent
<p>Link to Gitlab repo: https://gitlab.com/jezsmartinez/azobencene_ep/-/tree/main</p>
Quantum information phases in space-time: measurement-induced entanglement and teleportation on a noisy quantum processor
<p>Data for the manuscript at https://arxiv.org/abs/2303.04792</p>
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
<p>This is training dataset for our publication with title "Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics" at arXiv https://doi.org/10.48550/arXiv.2404.14021</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.