Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “Quantum Theory”
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>
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>
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>
Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory -- data release
<p>This submission contains the Markov-Chain Monte Carlo data required to reproduce central results of the paper "Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory" (<a href="https://arxiv.org/abs/2009.14768">https://arxiv.org/abs/2009.14768</a>). </p> <p>The Python code required to read and analyse the data can be found under https://github.com/andreasjuettner/Finite-Size-Scaling-Analysis (the relevant release is attached to 10.5281/zenodo.4290508).</p> <p>For any questions please get in touch: juettner@soton.ac.uk.</p>
Data files for "Hybrid quantum-classical approach for coupled-cluster Green's function theory"
<p>Source code and data files for the manuscript "Hybrid quantum-classical approach for coupled-cluster Green's function theory."</p> <p>Reference: Quantum 6, 675 (2022); https://doi.org/10.22331/q-2022-03-30-675.</p> <p>Title: Hybrid quantum-classical approach for coupled-cluster Green's function theory</p> <p>Authors: Trevor Keen, Bo Peng, Karol Kowalski, Pavel Lougovski, and Steven Johnston.</p> <p>Abstract: The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of dynamics and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system’s eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green’s function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to a wide range of models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grow as $O(N^5)$ per time step to calculate the impurity Green’s function in the time domain, where N is the total number of energy levels in the AIM. For comparison, a classical CCGF calculation of the same order would require computational resources that grow as $O(N^6)$ per time step.</p>
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>
SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019,
<p>SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019. This compliments the data set at doi: 10.17863/CAM.30358 (copy these SPAWN files here into relevant folder at histograms_toc_figs_1_2/H2O_3_ccpVDZ/).</p>
Supplementary data for "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory"
<p>Supplementary data belonging to "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory".</p> <p>The data includes geometries for the molecules in the paper as well as the Hamiltonian matrix elements, orbital coefficients and overlap matrices to reproduce the data in the paper.</p>
Roger Penrose Why Quantum Mechanics Is an Inconsistent Theory
<p>Roger Penrose Why Quantum Mechanics Is an Inconsistent Theory</p>
Mean-field theories are simple for neural quantum states
<p>Data used in the figures of the paper to appear under the title "Mean-field theories are simple for neural quantum states". All the data is written in csv files.</p>
Derivation of the Local Lorentz Gauge Transformation of a Dirac Spinor Field in Quantum Einstein-Cartan Theory
<p>I examine the groups which underly classical mechanics, non-relativistic quantum mechanics, special relativity, relativistic quantum mechanics, quantum electrodynamics, quantum flavourdynamics, quantum chromodynamics, and general relativity. This examination includes the rotations SO(2) and SO(3), the Pauli algebra, the Lorentz transformations, the Dirac algebra, and the U(1), SU(2), and SU(3) gauge transformations. I argue that general relativity must be generalized to Einstein-Cartan theory, so that Dirac spinors can be described within the framework of gravitation theory.</p>
Scaling theory of wave confinement in classical and quantum periodic systems
<p>Data repository to manuscript 'Scaling theory of wave confinement in classical and quantum periodic systems'. The upload contains the file "README.txt" explaining the content of the upload.</p>
GaAs quantum dots under quasi-uniaxial stress: experiment and theory (raw data)
<p>Here is the raw data of "GaAs quantum dots under quasi-uniaxial stress: experiment and theory ", we share this data to researchers aroud the world to test their theoretical models with our experimental data. The data contains two parts: the first one is AFM image of nano-holes on AlGaAs, which can be treated as the morphology of GaAs QDs studied in this work. The second part contains two typical polarization spectra of quantum dots under different stress. Meanwhile the polarization spectra of bulk GaAs on top of quantum dots are provided to estimate the stress precisely. </p>
Data from: Species distributions, quantum theory, and the enhancement of biodiversity measures
Open the record for dataset details and reuse information.
Unraveling the impact of nuclear quantum effects on proton affinity using nuclear electronic orbital-density functional theory: A Comprehensive Benchmark Study
Open the record for dataset details and reuse information.
Data for "Analytical evaluation of ground state gradients in quantum electrodynamics coupled cluster theory"
<p>Optimized geometries of selection of molecules in the paper.</p>
Data for "Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad84af" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citations is:</p> <p>[1] Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 225014</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Tabulated Data</li> <li>Misc</li> <li>Entanglement Entropy</li> <li>Appendix Data</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Tabulated Data" contains the data used in Table 1 and Table 2 in the paper).</p> <p>Each of these parent directories contain within them several sub-directories (child directories) corresponding to different produced data. The raw data can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>. The dictionary will have a parent key called "Energy", which in itself is yet another dictionary which will always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code>. The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p>Additionally, some child directories will include a <code>.npy</code> file, which holds the amplitudes of the variational state for the given simulation. These files should be loaded using numpy in python. For example:</p> <p><code>import numpy as np</code></p> <p><code>filePath = ...</code></p> <p><code>varState = np.load(filePath)</code></p> <p>This will load the amplitudes as an array into the <code>varState</code> variable.</p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p> <p> </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.