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954 results for “quantum”
Quantum-Chemical Bonding Database (Unprocessed data : Part 4)
<p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a>Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)</p>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Minimum dataset for "Liquid-activated quantum emission from pristine hexagonal boron nitride for nanofluidic sensing"
<p>Frames and (linked) localization table used to produce Fig. 2 of the manuscript https://www.nature.com/articles/s41563-023-01658-2.</p> <p>Details are given in 'README.txt'.</p> <p>The rest of the data is provided with the paper at the publisher website.</p>
Unveiling the atomistic and electronic structure of NiII–NO adduct in a MOF-based catalyst by EPR spectroscopy and quantum chemical modelling
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230706_01_CW_Xband </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_02_HYSCORE </strong>and <strong>PARACAT_WP4_20230706_03_ENDOR </strong>folders include X-band HYSCORE and ENDOR data; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_ 04_MATLAB</strong> and<strong> PARACAT_WP4_20230706_ 05_Modelling</strong> folders include matlab and computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> <li>File <strong>PARACAT_WP4_20230706_ 06_Origin</strong> include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MFU– </strong>MFU-4l:NO<sub>2</sub> MOF material</li> <li>NiNO – NO adsorbed MFU-4l:NO<sub>2</sub> MOF</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Towards near-term quantum simulation of materials
<p><strong>Overview of data provided in support of <em>Towards near-term quantum simulation of materials</em></strong></p> <p>Contents of this folder:</p> <ul> <li>`analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. </li> <li>`towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials.</li> <li>`towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript.</li> </ul> <p>The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, <br> i.e. the user need not run the script to produce the output files. <br> Users can generate these analyses directly by running ```python analyse_materials_results.py```, <br> though note the following packages will first need to be installed:</p> <ul> <li>`pandas >= 1.2.3`</li> <li>`numpy >= 1.23`</li> <li>`seaborn >= 0.11.1`</li> <li>`lfig >= 0.1.3`</li> <li>`matplotlib > 3.7.0`</li> </ul> <p><strong>Included data</strong><br> Within `towards_quantum_simulation_data`, there are subfolders <br> for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`.</p> <p>Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. </p> <ul> <li>`hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files <ul> <li>`interactions.json` Hamiltonian terms in terms of Majorana monomials.</li> <li>`map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated.</li> <li>`map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. </li> <li>`map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. </li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>`encoding` files which specify the fermionic encoding which is customised for the material under study. <ul> <li>`encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material.</li> <li>`precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. <ul> <li>The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`).</li> </ul> </li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>`algorithms` contains subfolders for each of the algorithms desribed in the manuscript <ul> <li>Those explored for circuits depths: <ul> <li>`TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript)</li> <li>`TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*)</li> <li>`VQESplitTerms` (VQE)</li> <li>`VQESplitTermsNoSwapNetwork` (VQE*)</li> </ul> </li> <li>each of which contain the files, inside the `circuitry` folder: <ul> <li>`circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit </li> <li>`circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit.</li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: <ul> <li>`MeasurementCommutativity` </li> <li>`MeasurementNaiveQubitwise`</li> <li>`MeasurementNonCrossing`</li> </ul> </li> <li>each of which contain the files, inside the `compilation` folder: <ul> <li>`layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14.</li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> </ul> </li> </ul> <p><strong>CSV files</strong></p> <p>In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials.</p> <ul> <li>`circuit_costs.csv`: results of running the circuit compiler described in the manuscript.</li> <li>`measurements.csv`: results of running the measurement compiler described in the manuscript.</li> </ul> <p>These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.</p>
Quantum yield of Photosystem II of Eriophorum vaginatum leaves in the reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon- Alaska in 2016
Quantum yield of Photosystem II estimated from chlorophyll fluorescence of Eriophorum vaginatum leaves from tussocks in the reciprocal transplant gardons at Toolik Lake, Coldfoot, and Sagwon in 2016. A single transplant tussock per plot was repeatedly measured through the season.
FIGURE 1 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>FIGURE 1 of the conference paper "Solid state synthesis of CdS quantum dots through laser direct writing"</p>
FIGURE 4 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>The dataset includes two files: the word file describe the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 4.</p> <p>in the following is reported the description of figure 4 as wrote in the paper.</p> <p>PL emission of CdDBX QDs synthetized with single pulses at 355 nm, 10 ps and pulse energy varied between 4.67 to 7.6 μJ. (a), (b) and (c) are respectively the emission in blue, green and red range as defined in methodology. Graph (d) shows the change of the ratio between the red and green emission from the same points.</p>
FIGURE 5 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>The dataset contains two files: the word file describe the type of sample examined and the procedure used to obtain the DOIs used to obtain the plots. The second file is the excel file that includes the raw data used to obtain the plots reported in the figure 5 of the paper.</p> <p>In the following is reported the description of figure 5 as written in the paper:</p> <p>PL emission of CdDBX QDs synthetized with scanlines at 355 nm, 10 ps overlapping pulse of about 99.5% and</p> <p>pulse energy varied between 4.67 to 7.6 μJ. (a), (b) and (c) are respectively the emission in blue, green and red range as defined</p> <p>in methodology. Graph (d) shows the change of the ratio between the red and green emission from the same point. Point “X” is</p> <p>censored and not used to fit the model.</p>
FIGURE 6 from paper JVST-B "Formation of CdSe quantum dots from single source precursor obtained by thermal and laser treatment"
<p>The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 6.</p> <p>In the following is reported the description of figure 6 as wrote in the paper.</p> <p>Absorption spectrum of PMMA/CdDMASe (black solid line), PMMA/CdDMASe/OA (red dashed line) and PMMA/CdDMASe/OAm (blue dotted line) films.</p>
Crossed graphene nanoribbons as beam splitters and mirrors for electron quantum optics
<p>OPEN DATA related to the research publication:</p> <p>S. Sanz, P. Brandimarte, G. Giedke, D. Sánchez-Portal, and T. Frederiksen, <em>Crossed graphene nanoribbons as beam splitters and mirrors for electron quantum optics</em>, Phys. Rev. B <strong>102</strong>, 035436 (2020) [arXiv:2005.11391]</p> <p>Abstract: We analyze theoretically 4-terminal electronic devices composed of two crossed graphene nanoribbons (GNRs) and show that they can function as beam splitters or mirrors. These features are identified for electrons in the low-energy region where a single valence or conduction band is present. Our modeling is based on <em>pz</em> orbital tight-binding with Slater-Koster type matrix elements fitted to accurately reproduce the low-energy bands from density functional theory calculations. We analyze systematically all devices that can be constructed with either zigzag or armchair GNRs in AA and AB stackings. From Green's function theory the elastic electron transport properties are quantified as a function of the ribbon width. We find that devices composed of relatively narrow zigzag GNRs and AA-stacked armchair GNRs are the most interesting candidates to realize electron beam splitters with a close to 50-50 ratio in the two outgoing terminals. Structures with wider ribbons instead provide electron mirrors, where the electron wave is mostly transferred into the outgoing terminal of the other ribbon, or electron filters where the scattering depends sensitively on the wavelength of the propagating electron. We also test the robustness of these transport properties against variations in intersection angle, stacking pattern, lattice deformation (uniaxial strain), inter-GNR separation, and electrostatic potential differences between the layers. These generic features show that GNRs are interesting basic components to construct electronic quantum optical setups.</p>
Storing single photons emitted by a quantum memory on a highly excited Rydberg state - Figure data
<p>The files contain the data associated with the paper Distante, Farrera et al. "Mapping single photons emitted by a quantum memory on a highly excited Rydberg state".</p> <p>The name of each file corresponds to the figure number (e.g. figure2a, figure3b, etc.) and the first element in each file (the first row) is the date in which the data has been taken. The data is the result of a pre-processing of histogram and time-stamping files in the experiment. On reasonable request, we can provide the raw data.</p> <p>The fits in the figures are referenced throughout the open-access paper, and can be found in the Supplementary Information of the publication.</p>
Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"
<p>Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", <em>npj Quantum Information</em><strong> 3</strong>, Article number: 16 (2017).</p>
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Data for "A Quantum Definition of Molecular Structure"
<p>Supplemental data for our article "A Quantum Definition of Molecular Structure".</p><p>Version 1.1.0 contains data for additional k-medoids runs performed on different subsets of the complete sample.</p>
Data for "An autonomous quantum machine to measure the thermodynamic arrow of time"
<p>Numerical simulation data from the article "An autonomous quantum machine to measure the thermodynamic arrow of time"</p> <p>J. Monsel, C. Elouard, A. Auffèves <em>npj Quantum Inf</em> <strong>4</strong>, 59 (2018). <a href="https://doi.org/10.1038/s41534-018-0109-8" target="_blank" rel="noopener">https://doi.org/10.1038/s41534-018-0109-8</a></p> <p>See the jupyter notebook for the data analysis and figures.</p> <p>The code to perform the numerical simulations is given in the repository <a href="https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system" target="_blank" rel="noopener">https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system</a>.</p>
Data underpinning "Engineering unsteerable quantum states with active feedback"
<div> <p>We provide the raw data used to produce plots shown in our paper "Engineering unsteerable quantum states with active feedback". The data is structured by: entangled state - number of qubits - target fidelity F*. For each parameter configuration 10 simulation runs were performed.</p> <p> </p> </div> <h2>Abstract</h2> <p>We propose active steering protocols for quantum state preparation in quantum circuits where each ancilla qubit (detector) is connected to a single system qubit, employing a simple coupling selected from a small set of steering operators. The decision is made such that the expected cost function gain in one time step is maximized. We apply these protocols to several many-qubit models. Our results are underlined by three remarkable insights. First, we show that the standard fidelity does not give a useful cost function; instead, successful steering is achieved by including local fidelity terms. Second, although the steering dynamics acts on each system qubit separately, entanglement in the generated target state is introduced, and can be tuned at will, by performing Bell measurements on ancilla qubit pairs after every time step. This implements a weak-measurement variant of entanglement swapping. Third, numerical simulations suggest that the active steering protocol can reach arbitrarily designated target states, including passively unsteerable states such as the N-qubit W state.</p>
Dataset underlying the manuscript: MAViS: Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
<p>Datasets underlying the manuscript. Information on how to run the scripts is detailed in the README file.</p>
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
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>
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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.