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954 results for “quantum”
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>
Data set for the journal article ''Nanoscale chemical reaction exploration with a quantum magnifying glass''
<div>This data set includes the raw data of the esterification and hydrogenation discussed in the journal article alongside with the Scine Puffin Singularity container, steering protocol files, Swoose parameters, (pre-)releases of the software, and Python scripts for individual steps without the graphical user interface to reproduce the data.</div>
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Exploring Large-Scale Entanglement in Quantum Simulation
<p>Here we provide data for the manuscript " <a href="https://arxiv.org/abs/2306.00057">Exploring Large-Scale Entanglement in Quantum Simulation</a> " with arXiv id <a href="https://arxiv.org/abs/2306.00057">"arXiv:2306.00057</a>". The data set contains both raw and analyzed data saved as ".mat files" Please see the uploaded readme file to understand the data structure. The peer-reviewed article will appear in the future. Please check the published article for recent figures. </p>
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</p>
Regularized quantum periods for four-dimensional Fano manifolds
<p><strong>The database smooth_fano_4</strong></p> <p>This is a database of regularized quantum periods for four-dimensional Fano manifolds. The database will be updated as new four-dimensional Fano manifolds are discovered and new regularized quantum periods computed.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper [CK2021]. If you make use of this data, please cite that paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708307</p> <p><strong>Names</strong></p> <p>The database describes Fano varieties via names, as follows:</p> <table> <caption>Names of Fano manifolds</caption> <thead> <tr> <th scope="col">Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>P1</td> <td>one-dimensional projective space</td> </tr> <tr> <td>P2</td> <td>two-dimensional projective space</td> </tr> <tr> <td>dP(k)</td> <td>the del Pezzo surface of degree k given by the blow-up of P2 in 9-k points</td> </tr> <tr> <td>P3</td> <td>three-dimensional projective space</td> </tr> <tr> <td>Q3</td> <td>a quadric hypersurface in four-dimensional projective space</td> </tr> <tr> <td>B(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 8k</td> </tr> <tr> <td>V(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 1, and degree k</td> </tr> <tr> <td>MM(r,k)</td> <td>the k-th entry in the Mori-Mukai list of three-dimensional Fano manifolds of Picard rank r, ordered as in [CCGK2016]<br> </td> </tr> <tr> <td>P4</td> <td>four-dimensional projective space</td> </tr> <tr> <td>Q4</td> <td>a quadric hypersurface in five-dimensional projective space</td> </tr> <tr> <td>FI(4,k)</td> <td>the four-dimensional Fano manifold of Fano index 3 and degree 81k</td> </tr> <tr> <td>V(4,k)</td> <td>the four-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 16k</td> </tr> <tr> <td>MW(4,k)</td> <td>the k-th entry in Table 12.7 of [IP1999] of four-dimensional Fano manifolds of Fano index 2 and Picard rank greater than 1</td> </tr> <tr> <td>Obro(4,k)</td> <td>the k-th four-dimensional Fano toric manifold in Obro's classification [O2007]</td> </tr> <tr> <td>Str(k)</td> <td>the k-th Strangeway manifold in [CGKS2020]</td> </tr> <tr> <td>CKP(k)</td> <td>the k-th four-dimensional Fano toric complete intersection in [CKP2015]</td> </tr> <tr> <td>CKK(k)</td> <td>the k-th four-dimensional Fano quiver flag zero locus in Appendix B of [K2019]</td> </tr> </tbody> </table> <p>A name of the form "S1 x S2", where S1 and S2 are names of Fano manifolds X1 and X2, refers to the product manifold X1 x X2.</p> <p><strong>References</strong></p> <p>[CCGK2016] <em>Quantum periods for 3-dimensional Fano manifolds</em>; Tom Coates, Alessio Corti, Sergey Galkin, Alexander M. Kasprzyk; Geometry and Topology 20 (2016), no. 1, 103-256.</p> <p>[CGKS2020] <em>Quantum periods for certain four-dimensional Fano manifolds</em>; Tom Coates, Sergey Galkin, Alexander M. Kasprzyk, Andrew Strangeway; Experimental Math. 29 (2020), no. 2, 183-221.</p> <p>[CK2021] <em>Databases of quantum periods for Fano manifolds</em>; Tom Coates, Alexander M. Kasprzyk; 2021.</p> <p>[CKP2015] <em>Four-dimensional Fano toric complete intersections</em>; Tom Coates, Alexander M. Kasprzyk, Thomas Prince; Proc. Royal Society A 471 (2015), no. 2175, 20140704, 14.</p> <p>[IP1999] <em>Fano varieties</em>; V.A. Iskovskikh, Yu. G. Prokhorov; Encyclopaedia Math. Sci. vol. 47, Springer, Berlin, 1999, 1-247.</p> <p>[K2019] <em>Four-dimensional Fano quiver flag zero loci</em>; Elana Kalashnikov; Proc. Royal Society A 275 (2019), no. 2225, 20180791, 23. </p> <p>[O2007] <em>An algorithm for the classification of smooth Fano polytopes</em>; Mikkel Obro; arXiv:0704.0049 [math.CO]; 2007.</p>
Regularized quantum periods for one-dimensional Fano manifolds
<p><strong>The database smooth_fano_1</strong></p> <p>This is a database of regularized quantum periods for one-dimensional Fano manifolds. There is one entry in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708188</p>
Coherence and indistinguishability of highly pure single photons from non-resonantly and resonantly excited telecom C-band quantum dots
<p>ABSTRACT</p> <p>The role of resonant pumping schemes in improving the photon coherence is investigated on InAs/InGaAs/GaAs quantum dots (QDs) emitting in the telecom C-band. The linewidths of transitions of multiple exemplary quantum dots are determined under above-band pumping and resonance fluorescence (RF) via Fourier-transform spectroscopy and resonance scans, respectively. The average linewidth is reduced from (9.74 ± 3.3) GHz in the above-band excitation to (3.50 ± 0.39) GHz under RF underlining its superior coherence properties. Furthermore, the feasibility of coherent state preparation with a fidelity of (49.2 ± 5.8) % is demonstrated, constituting a first step toward on-demand generation of coherent, single, telecom C-band photons directly emitted by QDs. Finally, two-photon excitation of the biexciton is investigated as a resonant pumping scheme. A deconvoluted single-photon purity value of 𝑔(2)HBT(0)=0.072 ± 0.104 and a postselected degree of indistinguishability of 𝑉HOM=0.894 ± 0.109 are determined for the biexciton transition. This represents another step in demonstrating the necessary quantum optical properties for prospective applications.</p>
Dataset for "Fast and efficient demultiplexing of single photons from a GaAs quantum dot with resonantly enhanced electro-optic modulators"
<p><strong>Dataset for "Fast and efficient demultiplexing of single photons from a quantum dot with resonantly enhanced electro-optic modulators"</strong></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</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>
Nonlinear two-level dynamics of quantum time crystals
<p>Dataset for the manuscript titled "AC Josephson effect between two superfluid time crystals" by the same authors.</p>
Experimental data for Quantum noise limited microwave amplification using a graphene Josephson junction
<p>This dataset was used in our study of "Quantum noise limited microwave amplification using a graphene Josephson junction".</p>
Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"
<p>This is a file collection as supplementary material for the paper <em>Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>.</em> It consists of:</p> <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> <p>The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in <em>Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a></em>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.</p>
Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
<p>Network parameters of continuous normalizing flows trained for the <span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract: We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the <span class="math-tex">\(\varphi^4\)</span> theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.</p>
Experimental data for the manuscript titled Rotating quantum wave turbulence
<p>This submission contains the minimal dataset required to reproduce the experimental findings related to the manuscript titled <em>Rotating quantum wave turbulence</em>, associated with the DOI 10.1038/s41567-023-01966-z.</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>
Dataset for the published article "Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids"
<p>The data contained in the zip file constitute the main research data of the article entitled as "<em>Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids</em>", published in the Journal of Chemical Physics as a Communication. In this article, a novel dielectric scheme is proposed for strongly coupled electron liquids that handles quantum mechanical effects beyond the random phase approximation level and treats electronic correlations within the integral equation theory of classical liquids. This self-consistent scheme features a complicated dynamic local field correction functional and yields unprecedently accurate results for the static structure factor without featuring any adjustable or empirical parameters.</p> <p>In particular, the datasets contain the static structure factors of the paramagnetic electron liquid as computed by four schemes of the self-consistent dielectric formalism and as extracted from state-of-the-art path integral Monte Carlo (PIMC) simulations. The dielectric schemes of interest are all tailor-made for the strongly coupled regime of the finite temperature uniform electron fluid (UEF; also known as jellium or quantum one-component plasma). These are the newly proposed quantum version of the integral equation theory based scheme (qIET), the newly proposed quantum version of the hypernetted-chain based scheme (qHNC), the integral equation theory based scheme (IET) [1,2] and the hypernetted-chain based scheme (HNC) [3,4].</p> <p>The static structure factors are provided for 20 paramagnetic UEF state points defined by (r<sub>s</sub>,Θ)={(50,0.50),(60,0.50),(70,0.50),(80,0.50),(90,0.50),(100,0.50),(100,0.75),(100,1.00),(100,2.00),(100,4.00),(110,0.50),(125,0.50),(125,0.75),(125,1.00),(125,1.50),(125,2.00),(150,0.50),(150,1.00),(200,0.50),(200,1.00)} where r<sub>s</sub> is the quantum coupling parameter and Θ is the degeneracy parameter. </p> <p>In the qIET, qHNC, IET and HNC datasets; the first column corresponds to the wavenumber normalized to the Fermi wavenumber and the second column corresponds to the static structure factor value. In the PIMC datasets, the first column corresponds to the wavenumber multiplied by the first Bohr radius, the second column corresponds to the static structure factor value and the third column corresponds to the associated error bars.</p> <p>[1] P. Tolias, F. Lucco Castello and T. Dornheim, J. Chem. Phys. 155, 134115 (2021).<br> [2] F. Lucco Castello, P. Tolias and T. Dornheim, EPL 138, 44003 (2022).<br> [3] S. Tanaka, J. Chem. Phys. 145, 214104 (2016).<br> [4] T. Dornheim, T. Sjostrom, S. Tanaka and J. Vorberger, Phys. Rev. B 101, 045129 (2020).</p>
Hybrid quantum-classical machine learning for generative chemistry and drug design: Generated molecules
<p>Deep generative chemistry models emerge as powerful tools to expedite drug discovery. How- ever, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The pre- sented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.</p>
ScienceDex guides
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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.