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56 results for “quantum simulation”
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>
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>
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>
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>
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data
<p>Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"</p>
Supporting data for "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array"
<p>Supporting data and analysis scripts for Fig. 3b of "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array", ArXiv:1702.07511 (preprint) and 10.1038/nature23022 (publication)</p> <p>This dataset contains a readme file as well as three zipped folders that contain (1) raw data sets of all relevant measurements, as well as (2) matlab files to plot fitted data and the extracted parameters and (3) the code that uses the extracted parameters to plot the fan diagram.</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>
Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku, Céline Toubin, and André Severo Pereira Gomes.</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>
Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'
<p>Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.</p>
Source Data for the paper: "Quantum-classical simulations reveal the photoisomerization mechanism of a prototypical first-generation molecular motor"
<p>This dataset contains the raw data for the results shown in the paper.</p> <p>For each figure of the paper (main text), one directory with data file(s) is provided.</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>
SIMULATION of: A Unified Model for WaveParticle Duality and Quantum Entanglement.pdf
<p>This Google Colab notebook contains the simulations used to validate the Fasano Symbiosis model, as presented in the paper "A Unified Model for Wave-Particle Duality and Quantum Entanglement." The simulations explore how entangled particles interact and propagate information through their combined wave function. The notebook provides an interactive environment where users can run the simulations and explore the impact of different energy interactions on particle wavelengths, quantum entanglement, and wave function collapse. The results from these simulations align with the theoretical predictions discussed in the paper and offer insights into quantum communication and cryptography.</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>
Controlling long ion strings for quantum simulation and precision measurements
<p>Experimental data for the publication "Controlling long ion strings for quantum simulation and precision measurements" published in Physical Review A, 105, 052426 (2022)</p>
Data for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et al.
<p>Data and python scripts for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et <em>al</em>.</p> <p>Corresponding authors Corentin Déprez (C.C.Deprez@tudelft.nl) // Menno Veldhorst (M.Veldhorst@tudelft.nl)</p>
Dataset for: Quantum cascade lasers with discrete and non equidistant extended tuning tailored by simulated annealing
<p>Dataset used for article 10.1364/OE.27.026701.</p>
Dataset containing raw simulation data for a paper on decoding bosonic quantum LDPC codes
<p>This is the dataset from evaluations used in code related to a paper on analog information decoding of bosonic quantum LDPC codes, available on Github (https://github.com/cda-tum/mqt-qecc/). For more information we refer to the Github repository and the paper.</p>
Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization
<p>Data repository for "Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization" <a href="https://arxiv.org/abs/2403.03502" target="_blank" rel="noopener">arXiv:2403.03502</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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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.