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46 results for “quantum dynamics”
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>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
Scalably learning quantum many-body Hamiltonians from dynamical data
<p>Our <a href="https://arxiv.org/abs/2209.14328">paper</a> on Hamiltonian learning for large quantum systems contains several numerical results. The results were produced with the <a href="https://github.com/frederikwilde/differentiable-tebd">differentiable-tebd package</a> which we developed for this study. The scripts and raw output data, as well as Jupyter notebooks for generating the plots shown in the paper are contained in this repository. For more information please refer to the <a href="https://github.com/frederikwilde/scalable-dynamical-hamiltonian-learning/">guiding repository</a>.</p> <p>For funding information please refer to the acknowledgement section of the <a href="https://arxiv.org/abs/2209.14328">paper</a>.</p>
Data of publication "Simulating the dynamics of large many-body quantum systems with Schrödinger-Feynman techniques"
<p>The development of powerful numerical techniques has drastically improved our understanding of quantum matter out of equilibrium. Inspired by recent progress in the area of noisy intermediate-scale quantum devices, this paper highlights hybrid Schrödinger-Feynman techniques as an innovative approach to efficiently simulate certain aspects of many-body quantum dynamics on classical computers. To this end, we explore the nonequilibrium dynamics of two large subsystems, which interact sporadically in time, but otherwise evolve independently from each other. We consider subsystems with tunable disorder strength, relevant in the context of many-body localization, where one subsystem can act as a bath for the other. Importantly, studying the full interacting system, we observe that signatures of thermalization are enhanced compared to the reference case of having two independent subsystems. Notably, with the here proposed Schrödinger-Feynman method, we are able to simulate the pure-state survival probability in systems significantly larger than accessible by standard sparse-matrix techniques.</p> <div> </div> <div> </div>
Raw data to "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions"
<div> <p>This directory contains the data used to generate the numerical results in the work "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions [1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>[1]: P. Adelhardt, A. Duft and K. P. Schmidt, Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions, <a href="https://arxiv.org/abs/2408.13145">arXiv:2408.13145</a></p> <p> </p> </div>
Dataset for Dynamic Analysis of Quantum Annealing Programs
<p>Quantum software engineering is emerging as a relevant field as it deals with the challenges of producing the new quantum software, whose adoption is increasing progressively. One of those challenges is how quantum software is migrated, how it operates in combination with classical software, or how it should be maintained. In this context this research focuses on reverse engineering of quantum annealing software to facilitates its integration in hybrid software systems. Quantum annealing software has gained a certain market penetration, demonstrating a good performance for optimization problems. While there are some preliminary reverse engineering techniques for gate-based quantum software, there is no reverse engineering techniques to discover the underlying optimization problem definitions (Hamiltonians functions to be minimized). Problem definitions are, in turn, dynamically defined through classical software, and can evolve over time, which make it difficult its accurate comprehension and abstract representation. Thereby, this paper presents a dynamic analysis technique for D-Wave (python) programs for reversing Hamiltonians expressions, that are additionally represented according to the Knowledge Discovery Metamodel. Due to the usage of this standard, the reversed Hamiltonians can be represented in combination with other parts of classical-quantum software systems. In order to facilitates its adoption, the proposed technique has been empirically validated through a case study with 27 D-Wave programs that demonstrates the effectiveness and efficiency. This dataset includes measures derived from that case study.</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>
Companion Dataset for Manuscript "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device"
<p>This dataset provides the data necessary to reproduce the figures in the manuscript titled "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device". Combined with the cQED4ChemDyn code, provided through Github and Zenodo, it regenerates the figures in the manuscript.</p> <p>The project itself provides an implementation that connects chemical kinetics of elementary reactivity models with the framework of the Kerr-Cat circuit quantum electrodynamics (cQED), using the Hamiltonian describing the physics of the hardware and a Lindbladian open quantum dynamics formalism for the time-evolution of the system. For more information, check the existing citation for the publication; any usage of the code/data should cite the preprints (and publications) once available.</p>
Dataset for "Experimental Quantum Simulation of Chemical Dynamics"
<p>The CSV files contain experimental data corresponding to figure 3 of the paper T. Navickas et al., "Experimental Quantum Simulation of Chemical Dynamics", arXiv:2409.04044 (2024)</p> <p>The contents of the files are described in README.txt.</p>
Data for: Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation
<p>Data for publication: J. Roulet, S. Choi, J. Vanicek, Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation, J. Chem. Phys. <strong>150</strong>, 204113 (2019)</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Quantum critical dynamics in a 5000-qubit programmable spin glass: data repository
<p>Supporting data for "Quantum critical dynamics in a 5000-qubit programmable spin glass", Nature, 2023.</p>
Data for S. Grandi et al., "Quantum dynamics of a driven two-level molecule with variable dephasing", arXiv:1607.02112, to appear in Phys. Rev. A.
<p>Data for S. Grandi et al., "Quantum dynamics of a driven two-level molecule with variable dephasing", arXiv:1607.02112, to appear in Phys. Rev. A. We include raw data, a Mathematica notebook, and read me files describing the files.</p>
The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model
<p>Data in the form of mx and dat files for "The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model", T. Masłowski, and N. Sedlmayr<br>Journal of Physics: Condensed Matter 36, 335401 (2024), <a href="https://doi.org/10.1088/1361-648X/ad4a16">https://doi.org/10.1088/1361-648X/ad4a16</a>.</p> <p>Included are data for the return rate (RR), Fisher zeroes, and Loschmidt eigenvalues (MEV). Numbers in parentheses refer to {100m,100m'} and Lx and Ly are the system sizes. Within the return rate files the third column is the derivative of the return rate.</p>
Vortex loop dynamics and dynamical quantum phase transitions in 3D fermion matter
<p>Supplemental Material and data corresponding to the article 'Vortex loop dynamics and dynamical quantum phase transitions in 3D fermion matter'.</p> <p>File description:</p> <ul> <li>_sm.pdf - Supplemental Material to the article</li> <li>fig_2_rate.txt - rate function corresponding to Fig. 2</li> <li>fig_3_rate.txt - rate function $\lambda$ corresponding to Fig. 3</li> <li>fig_3_rate_G.txt - rate function $\lambda_G$ corresponding to Fig. 3</li> <li>fig_3a.txt - phase of the Green's function corresponding to Fig. 3a</li> </ul> <p> </p>
Data archive for the publication: Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach
<p>The two compressed repositories contain raw and analysed data for the following publication on Optics Express: <strong><a href="https://doi.org/10.1364/OE.452874">Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach</a></strong></p> <p>The code for processing and analysing these data is available on GitHub and it has its own DOI. </p> <p>To access the code and for instructions on how to run the it follow the link: <a href="https://github.com/Biophotonics-Tyndall/QY-System-paper">Biophotonics-Tyndall/QY-System-paper (github.com)</a>. </p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Rumble, C.; Vauthey, E. Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations. Phys. Chem. Chem. Phys. 21 (2019). 10.1039/C9CP00795D</p>
Quantum Chemical Topology of the Electron Localization Function in the Field of Attosecond Electron Dynamics
<p>Movies accompaning publication of the Article "Quantum Chemical Topology of the Electron Localization Function in the Field of Attosecond Electron Dynamics". Journal of Physical Chemistry Letters, ACS, DOI:10.1021/acs.jpclett.7b03379</p> <p><strong>Collision of water molecule by alpha particle. Time-Dependent Density Functional Theory simulations.</strong></p> <p><strong>Water molecule</strong></p> <p>- Collision of gas phase H<sub>2</sub>O with 100 MeV alpha particle. ELF isosurface of 0.88.</p> <p>Full duration of movie: 5fs. 1E2MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 1 MeV alpha particle. ELF isosurface of 0.88.</p> <p>Full duration of movie: 5fs. 1E0MeV_H2O_view1.mpeg</p> <p> </p> <p>- Collision of gas phase H<sub>2</sub>O with 0.1 MeV alpha particle. ELF isosurface of 0.70.</p> <p>Full duration of movie: 5fs. 1E-1MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 0.05 MeV alpha particle. ELF isosurface of 0.8.</p> <p>Full duration of movie: 5fs. 5E-2MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 0.01 MeV alpha particle. ELF isosurface of 0.8.</p> <p>Full duration of movie: 5fs. 1E-2MeV_H2O_view1.mpeg</p> <p><strong>Movies - guanine </strong></p> <p>Color code: Carbon in green, oxygen in red, nitrogen in blue, hydrogen and alpha particle in white. The ELF attractor are shown in yellow.</p> <p> </p> <p>- Collision of gas phase guanine with 1 MeV alpha particle. ELF isosurface of 0.8. Case i) the alpha particle impacts the central C=C bond. Full duration of movie: 430as. </p> <p>gua_1MeV_C=C_view1.mpeg, gua_1MeV_C=C_view2.mpeg</p> <p>- Collision of gas phase guanine with 1 MeV alpha particle. ELF isosurface of 0.8. Case ii) the alpha particle impact the 6-member ring N atom. Full duration of movie: 430as. </p> <p>gua_1MeV_6RN_view1.mpeg, gua_1MeV_6RN_view2.mpeg</p> <p>- Collision of gas phase guanine with 0.1 MeV alpha particle. ELF isosurface of 0.8. Case i) the alpha particle impacts the central C=C bond. Full duration of movie: 1.5fs. </p> <p>gua_01MeV_C=C_view1.mpeg, gua_01MeV_C=C_view2.mpeg</p> <p>- Collision of gas phase guanine with 0.1 MeV alpha particle. ELF isosurface of 0.8. Case ii) the alpha particle impact the 6-member ring N atom. Full duration of movie: 1.5fs. </p> <p>gua_01MeV_6RN_view1.mpeg, gua_01MeV_6RN_view2.mpeg</p>
Data for "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling"
<p>We provide the data and scripts used to produce the figures shown in our publication "Entanglement Dynamics in Monitored Kitaev Circuits:<br>Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling".</p>
Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models
<p>Data for the publication <em>Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models</em>. Included are data for return rates and Fisher zeroes. The names of the data files contain all metadata necessary for specifying the type of data, with a list of the paramters used. For more details on the models and paramters see the article.</p> <ul> <li>Firstly the three models are considered labelled by <em>Kit</em> for the 2D spinless px+ipy topological superconductor, <em>Sq</em> for the 2D spinfull topological superconductor, and <em>3D</em> for the 3D topological insulator.</li> <li>After <em>_N_</em> the system size is written, referring the number of sites along one direction (for finite size calcualtions only).</li> <li><em>RR</em> refers to the return rate. For <em>Kit</em> the columns are $t$, $l(t), $\dot{l}(t)$, $\ddot{l}(t)$. If only two columns exit then there is just $t$, $\dot{l}(t)$. For <em>Sq</em> the columns are $t$, $l(t), $\dot{l}(t)$, $|\lambda_0(t)|$. For <em>3D</em> the columns are $t$, $\dot{l}(t)$, $\ddot{l}(t)$.</li> <li><em>Density</em> refers to the density of Fisher zeroes along the real time axis, with the columns being simply time and density.</li> <li><em>Fisher</em> to Fisher zeroes in the complex plane with <em>kx</em>, etc meaning it is resolved along this momentum direction. The columns are the real and imaginary parts of the complex time argument.</li> <li><em>Critical_k</em> are the critical momenta.</li> <li><em>Critical_k_En</em> lists the energy for each critical momenta in the matching <em>Critical_k </em>file.</li> <li>For <em>Kit</em> the list of quench parameters are 100 times {$\mu_0$, $\Delta_0$, $\mu_1$, $\Delta_1$}.</li> <li>For <em>Sq</em> the list of quench parameters are 100 times {$\nu_0$, \nu_1$, $\mu_0$, $\Delta_0$, $\alpha_0$, $B_0$, $\mu_1$, $\Delta_1$, $\alpha_1$, $B_1$}.</li> <li>For <em>3D</em> the list of quench parameters are 100 times {$v_0$,$w_0$,$v_1$,$w_1$}.</li> <li>For <em>Fisher</em> data <em>branch</em> labels naturally the branch p.</li> <li><em>Zoom</em>, etc, label different zooms in differnet regions, as made explicit by the times inside the files.</li> </ul> <p>This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625, by the German Research Council (DFG) via the Re- search Unit FOR 2316 and by the National Science and Engineering Resource Council (NSERC) of Canada via the Discovery Grant program.</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.