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79 results for “Quantum state”
XAlkeneDB: A database illuminating the electronic ground and excited state quantum chemical features of ethene, propene and butene
<div> <div> <div> <p>The dataset associated with this research has been published in <a href="https://pubs.rsc.org/en/content/articlelanding/2024/sc/d4sc04164j" target="_blank" rel="noopener"> Chem. Sci., 2024,15, 15880-15890.</a> Please cite this journal article when using the data.</p> </div> </div> </div>
Bayesian analysis of the equation of state of quantum chromodynamics from a holographic model
<p>Prior and posterior samples obtained from a Bayesian analysis of the equation of state of quantum chromodynamics (QCD) within a holographic Einstein-Maxwell-Dilaton model, constrained by state-of-the art lattice QCD results at a vanishing net density of baryons.</p> <p>Samples contain metadata, model parameters, and model predictions for the location of the QCD critical point.</p> <p>Supplement to <a title="Bayesian location of the QCD critical point from a holographic perspective" href="https://arxiv.org/abs/2309.00579">arXiv:2309.00579</a>.</p>
Supporting data for "Entanglement between a Telecom Photon and an On-Demand Multimode Solid-State Quantum Memory"
<p>This repository contains the data supporting the article "Entanglement between a Telecom Photon and an On-Demand Multimode Solid-State Quantum Memory" by Jelena V. Rakonjac, Dario Lago-Rivera, Alessandro Seri, Margherita Mazzera, Samuele Grandi and Hugues de Riedmatten, Phys Rev Lett 2021.</p> <p>The data files used for the figures in the main text are included here, as well as a version of the final article submission.</p>
Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement
<p><strong>Dataset for "Beyond the four-level model: Dark and hot states in quantum dots degrade photonic entanglement"</strong></p> <p><em>Nano Lett.</em> 2023, 23, 4, 1409–1415<br> Publication Date: February 6, 2023<br> <a href="https://doi.org/10.1021/acs.nanolett.2c04734">https://doi.org/10.1021/acs.nanolett.2c04734</a></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p> <p><strong>Data reuse</strong><br> Please cite B.U. Lehner et al., <em>Nano Lett.</em> 2023, 23, 4, 1409–1415 (2023) in publications that reuse this data and if possible inform the corresponding authors.</p>
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>
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>
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>
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 "Dispersive sensing of charge states in a bilayer graphene quantum dot"
<p>Supporting data and analysis scripts for all figures in the article "Dispersive sensing of charge states in a bilayer graphene quantum dot", Appl. Phys. Lett. <strong>118</strong>, 093104 (2021); <a href="https://doi.org/10.1063/5.0040234">https://doi.org/10.1063/5.0040234</a></p> <p>The files are sorted according to the figures/panels in the publication with a "0-README.txt" file including further information. </p> <p>The following versions of Pyhton and the packages have been used:<br> python: 3.6.10<br> numpy: 1.18.1<br> matplotlib: 3.1.3<br> scipy: 1.4.1</p>
Supplemental Data for "Self-similarity of spectral response functions for fractional quantum Hall states"
<p>Scripts and data to supplement the paper "Self-similarity of spectral response functions for fractional quantum Hall states".</p>
Cryogenic feed-forward of a Photonic Quantum State
<p>#################################################################################<br>#################################################################################<br>#################################################################################<br>Supplemental material for 'Cryogenic feed-forward of a Photonic Quantum State'<br>#################################################################################<br>#################################################################################<br>#################################################################################</p> <p><br>#################################################################################<br>Simulation:<br>#################################################################################<br>simulation.py<br> - simulation script used to calculate expected g(2)(0) values for various experimental settings<br> - uncomment the the parts of the setting you want to plot<br> - the POVM matrix of the 4 pixel SNSPD calculated by Timon Schapeler<br> - used to calculate the response of the 4 pixel detector to a given photon number</p> <p><br>#################################################################################<br>G2 Measurements:<br>#################################################################################</p> <p>Coincidences_xxx.csv<br> bidirectional coincidence Histogramms of various events (see below) at an idler mean photon number<br> The collums indicate the number of counts in a given timebin<br> timebins are indicative of the relative time difference between two events<br> binsize: 250ps<br> <br> Row contents are:<br> no photon number resolution<br> 0: coincidence HBT detector 1 and modulator trigger<br> 1: coincidence HBT detector 2 and modulator trigger<br> 2: coincidence HBT detector 2 and virtual channel of detector 1 heralded with the modulator trigger<br> 3: coincidence HBT detector 1 and virtual channel of detector 2 heralded with the modulator trigger<br> 4: coincidence HBT detector 1 and detector 2<br> selecting single photons<br> 5: coincidence HBT detector 1 and modulator trigger<br> 6: coincidence HBT detector 2 and modulator trigger<br> 7: coincidence HBT detector 2 and virtual channel of detector 1 heralded with the modulator trigger<br> 8: coincidence HBT detector 1 and virtual channel of detector 2 heralded with the modulator trigger<br> 9: coincidence HBT detector 1 and detector 2<br> selecting two photons<br> 10: coincidence HBT detector 1 and modulator trigger<br> 11: coincidence HBT detector 2 and modulator trigger<br> 12: coincidence HBT detector 2 and virtual channel of detector 1 heralded with the modulator trigger<br> 13: coincidence HBT detector 1 and virtual channel of detector 2 heralded with the modulator trigger<br> 14: coincidence HBT detector 1 and detector 2</p> <p>#################################################################################<br>Cryogenic Electronics<br>#################################################################################</p> <p>Threshold Sweep Map.csv<br> observed countrate under pulsed coherent state input. The repetition rate 600kHz for the pulsed 1550nm laser.<br> axis: Threshold settings ranging from 30mV to 150mV in 2mV steps<br> The columns correspond to a sweep of the high level threshold.<br> The rows correspond to a sweep of the low level threshold.</p> <p>#################################################################################<br>Source:<br>#################################################################################</p> <p>SHG_Spectrum.csv<br> The resulting spectrum of the SHG conversion with the periodically poled LiNbO3 crystal. The intensity is normalized to the maximum amplitude.</p> <p>JointSpectrum_Wavelength.csv<br>JointSpectrum_Countrate.csv<br> Dataset of the joint spectral intensity in counts/s. The used incremental wavelength steps are given in the file. <br> The presented joint spectrum is acquired after the PDC-process.</p> <p>#################################################################################<br>Modulator:<br>#################################################################################</p> <p>ModulatorSweep_Output.csv<br>ModulatorSweep_Voltage.csv<br>ModulatorSweep_Wavelength.csv<br> Characterisation scan of the cryogneic electro-optic modulator. The wavelength and voltage of modualtor is swept while the throughput power is acquired.<br> The dataset is acquired with a narrow band laser with a FWHM of 1pm. The dataset is plotted in the supplementary material.</p> <p>Time_ModulationTrace.csv<br>DrivingSignal_ModulationTrace.csv<br>ResponsSignal_ModulationTrace.csv<br> Optical response of the croygenic photonic circuit when light is detected in the idler arm. The driving signal is generated by the SNSPD, amplifier, discriminator and modulator driver.<br> The driving signal is acquired from the cryogneic circuit into a room temperature 50Ohm load. <br> The response signal of is acquired by sending constant power through the electro-optic modulator at 1550nm.<br> The driving signal is generated by the complete cryogenic SNSPD, amplifier, discriminator and modulator driving connected to the modulator.<br> The signal is acquired with a room temperature photodiode. <br> The time steps are given in the file.</p> <p>modulator_S_parameters.csv<br> The measured S parameters of the modulator measured with CW light and a photodiode<br> Collums are: frequency [MHz] : S11 amplitude [dB] : S11 Phase [Degrees] : S21 amplitude [dB] : S21 Phase [Degrees]</p> <p>#################################################################################<br>4-pixel SNSPD:<br>#################################################################################</p> <p>Traces_SNSPDTraces.csv<br>Time_SNSPDTraces.csv<br> 1000 signal traces at the output of the SNSPD. These presented traces are acquired after the cryogenic amplifier with a room temperature oscilloscope with a 50Ohm load.<br> The signals time steps are given in the second file. The dataset is displayed in figure 2b) of the main manuscript. </p> <p>Detector_bias_countrate.csv<br> countrate observed after the triggering electronics when scanning the SNSPD bias current. SNSPD illuminated with pulsed 600kHz repetition rate attenuated coherent state.<br> Collums are: Voltage over 110 k ohm biassing resistor [mV] : countrate when illuminated : countrate when input blocked</p> <p> </p> <p>#################################################################################<br>Amplifiers:<br>#################################################################################</p> <p>first_amp_high_power_S_parameters.csv<br> S-parameters for the first amplifier stage operated at ∼50 μA Base current and 0.6 V at the collector<br>first_amp_low_power_S_parameters.csv<br> S-parameters for the first amplifier stage operated at ∼17 μA Base current and 0.4 V at the collector<br>second_amp_S_parameters.csv<br> S-parameters for the second amplifier stage</p> <p>all files:<br> Collums are: frequency [MHz] : S11 amplitude [dB] : S11 Phase [Degrees] : S21 amplitude [dB] : S21 Phase [Degrees] : S12 amplitude [dB] : S12 Phase [Degrees] : S22 amplitude [dB] : S22 Phase [Degrees]</p> <p><br>#################################################################################<br>unbinned counts:<br>#################################################################################<br> <br>unprocessed_coincidences_no_modulator.csv<br> measred coincidences between the 4 pixel SNSPD and a detector in the HBT interferometer when entirely bypassing the modulator<br>unprocessed_coincidences_no_modulator.csv<br> measred coincidences between the 4 pixel SNSPD and a detector in the HBT interferometer when going through the modulator</p> <p>collumns: relative time [ps] : counts</p> <p> </p> <p> </p> <p> </p>
Supporting data for "Multipartite entangled states in dipolar quantum simulators"
<p>Supporting data for "Multipartite entangled states in dipolar quantum simulators" (https://arxiv.org/abs/2205.03910.), by Tommaso Comparin, Fabio Mezzacapo and Tommaso Roscilde. If you use these data in a scientific work, please cite the corresponding article.</p> <p>This dataset includes tVMC and exact results for the two-dimensional dipolar XX model, on square or triangular (periodic) lattices.</p> <p>These results are directly shown in Figures 1-5 of the main text, with the following organization:<br> - See Fig_collective_spin_dynamics for Fig. 1(b); see Fig_GHZ_fidelity for Fig. 1(d).<br> - See Fig_spin_squeezing for Fig. 2.<br> - See Fig_full_counting_statistics_N20 for Fig. 3.<br> - See Fig_Fisher_information_inequality for Fig. 4.<br> - See Fig_GHZ_fidelity for Fig. 5.</p> <p>We also include the exact-diagonalization results shown in Fig. 7 of the Supplementary Material -- see Fig_tower_of_states.</p>
Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons
<p>Here we upload the raw data from the manuscript entitled “Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons ”, by T. Röper, H. Thomas, D. Rosenbach, A. Uday, G. Lippertz, A. Denis, P. Morfin, A.A. Taskin, Y. Ando and E. Bocquillon. We provide Jupyter notebooks to load, process, and plot all results. The datasets contain measurements on 4 devices. Each device has its own Jupyter notebook. The necessary Python packages are listed in the file called "requirements.txt". <br><br><br></p>
Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field
<p>We provide the raw data used to produce Figs.3-6-7-8-9-10-11 of our paper "Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field"</p>
The influence of lattice termination on the edge states of the quantum spin Hall insulator monolayer 1T'-WTe_2
<p>We study the influence of sample termination on the electronic properties of the novel quantum spin Hall insulator monolayer 1T'-WTe<sub>2</sub>. For this purpose, we construct an accurate, minimal 4-orbital tight-binding model with spin-orbit coupling by employing a combination of density-functional theory calculations, symmetry considerations, and fitting to experimental data. Based on this model, we compute energy bands and 2-terminal conductance spectra for various ribbon geometries with different terminations, with and without magnetic field.</p> <p>The provided repository contains all code and data to reproduce the results of this study.</p>
Data for "Realizing topologically ordered states on a quantum processor"
<p>Data and code used in "Realizing topologically ordered states on a quantum processor," Satzinger et al. (2021), preprint at https://arxiv.org/abs/2104.01180</p>
Control of Electron State Coupling in Asymmetric Ge/GeSi Quantum Wells - DataSet
<p>Relevant dataset (FTIR data as TM/TE vs energy) for the publication "Control of electron state coupling in asymmetric Ge/GeSi quantum wells" published in Physical Review Applied (10.5281/zenodo.2319242)</p>
Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature
<p>This dataset contains processed data corresponding to the figures in the main text of our paper "Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature"</p> <p>The dataset consists of 10 .csv files and a jupyter notebook for generating the figures in the main text.</p>
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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.