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25 results for “Quantum correlations”
Numerical data for "Nonlocal correlations transmitted between quantum dots via short topological superconductor"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Nonlocal correlations transmitted between quantum dots </em><em>via short topological superconductor</em> (arXive preprint <a href="http://arxiv.org/abs/2405.06630">http://arxiv.org/abs/2405.06630</a>), and other data obtained within the same project. See file headers for details concerning the content.</p>
Data-driven criteria for quantum correlations
<p>Dataset of 3-qubit density matrices used for training deep-learning models for quantum correlations identicfication introduced in two papers:</p> <ul> <li> <div>M Krawczyk, J Pawłowski, MM Maśka, K Roszak Physical Review A 109, 022405 (2024).</div> <div> </div> </li> <li> <div>J Pawłowski, M Krawczyk, Physical Review Applied 22, 014068 (2024).</div> <div> </div> </li> </ul>
Distributing quantum correlations through local operations and classical resources
<p>Text files containing data of the figures shown in "Distributing quantum correlations through local operations and classical resources". Where some variables do not affect the plot value, for instance the values of φ in Figures 4a and 4b, fewer plot points of these variables are used in the final heatmaps to allow more detail in the other variables which do affect the function values. https://arxiv.org/abs/2408.05490. </p>
Machine learning the quantum flux-flux correlation function for catalytic surface reactions
<p>This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination.</p> <p><strong>reaction_details.csv</strong></p> <p>This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55.</p> <p> </p> <p>Column descriptions:</p> <ul> <li>reaction_number: Reaction identifier number used in this work</li> <li>reaction: The chemical reaction equation</li> <li>metal_surface: atomic symbol of metal surface</li> <li>facet_number: Miller indices of surface</li> <li>reactants: Python dictionary object of reactants and their quantities</li> <li>products: Python dictionary object of products and their quantities</li> <li>reaction_energy [eV]: reaction energy in electron-volts</li> <li>activation_energy [eV]: activation energy of reaction in electron-volts</li> <li>temperature [K]: The randomly assigned temperature a calculation was run for</li> <li>kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time).</li> <li>reaction_split: Train/test placement of that reaction/temperature combination for reaction split</li> <li>temperature_split:<strong> </strong>Trian/test placement of that reaction/temperature combination for temperature split</li> <li>catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database</li> <li>doi:<strong> </strong>digital object identifier of original publication for which DFT calculations were performed</li> </ul> <p> </p> <p> </p> <p><strong>Flux_flux_correlation_functions:</strong></p> <p>Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)<sup>2</sup>].</p> <p> </p> <p><strong>Geometries:</strong></p> <p>Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).</p> <p> </p> <p> </p>
Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
<p>Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), <a href="https://arxiv.org/abs/2310.02309">¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309</a></p> <p>This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (<a href="https://github.com/CarlosSMWolff/ParamEst-NN">github.com/CarlosSMWolff/ParamEst-NN</a> ) and reproduce the results shown in the paper.</p> <p>The dataset consist of four folders:</p> <ol> <li><strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the <a href="https://qutip.org/">QuTiP</a> library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps.</li> <li><strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper.</li> <li><strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the <a href="http://github.com/CarlosSMWolff/ParamEst-NN">repository</a>).</li> <li><strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling.</li> </ol> <p> </p> <p><em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em></p>
Data for "Characterizing quantum gases in time-controlled disorder realizations using cross-correlations of density distributions"
<p>Here, data sets belonging to the preprint "Characterizing quantum gases in time-controlled disorder realizations using cross-correlations of density distributions" are uploaded.</p> <p>The file contains a folder for each figure in the manuscript.</p>
Raw and processed data for 'Purification-based quantum error mitigation of pair-correlated electron simulations'
<p>Experimental data for 'Demonstration of purification-based error mitigation for quantum simulation'</p> <p>This directory contains two folders - 'final_data_sets' and 'plots_for_paper'.<br> 'final_data_sets' contains directories with raw experimental data, either in the form of raw shots, or accumulated expectation values. Everything is stored in either json or pickle format. This data was processed to generate the datasets in subfolders of 'plots_for_paper', which also contains notebooks for generating plots in the paper (data processing scripts to appear soon). Note that the python file to generate Fig.3 is in 'plots_for_paper/paper_plot_ring_opening_sixq_10q'.</p> <p>For any further questions, please email teobrien@google.com.</p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
<p>Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.</p>
Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"
<div>Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.</div> <h3>main scripts</h3> <div> <p><strong><em>generate_activity_dataset.py</em></strong></p> <p>reshape data in <em>neuronData.npy</em> to 50k samples of (neurons, timesteps) shape, saved in <em>activity_data.npy</em></p> <p><strong><em>create_target_distributions.py</em></strong></p> <p>based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in <em>distribution_target_dictionary.pkl</em></p> <p><strong><em>create_hyperparameters_file.py</em></strong></p> <p>generates <em>hyperparameters.csv</em>, containing:</p> </div> <ul> <li>number of neurons</li> <li>number of timesteps</li> <li>number of auxiliary_qubits</li> <li>batch_size</li> <li>learning rate of generator</li> <li>learning rate of critic</li> <li>number parametrized layers</li> <li>number of training iterations</li> <li>loss type</li> </ul> <p>for each run</p> <p><strong><em>train_qgan.py</em></strong></p> <div> <p>trains models defined <em>models.py</em> using <em>activity_data.npy</em> dataset, and for the hyperparameters defined in <em>hyperparameters.csv</em></p> </div> <div>saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file</div> <div> </div> <div><strong><em>generate_fake_activity.py</em></strong></div> <div> </div> <div>uses trained models saved in <em>output/models/run{run}/i{training_step}.pth</em> for a specific <em>training_step</em> and <em>run</em> to generate fake data, and save them in <em>output/generated_data/run{run}/i{training_step}.npy</em> files</div> <div> </div> <div><strong><em>analyze_error.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate two statistical quantities (k-probs and firing rate), using the function in <em>metrics.py</em>, and compare the errors in those quantities between the models using k-loss and standard-loss</div> <div> </div> <div><strong><em>analyze_stats.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate:</div> <ul> <li>js diverge for each training step, and final distribution of generated states, stored in <em>distribution_target_dictionary.pkl</em></li> <li>other statistical quantities, using the function in <em>metrics.py</em> file</li> </ul> <h3>auxiliary scripts</h3> <div><strong><em>metrics.py</em></strong></div> <div> </div> <div>functions to calculate neuronal statistics</div> <div> </div> <div><strong><em>aux.py</em></strong></div> <div> </div> <div>auxiliary functions:</div> <ul> <li>to generate states distribution given a dataset</li> <li>custom js divergence</li> </ul> <h3>Data</h3> <p><strong><em>neuronData.npy</em></strong></p> <p>neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)</p> <p><strong><em>activity_data.npy</em></strong></p> <p>dataset obtained from <em>neuronData.npy</em>, taking 50 thousand samples of shape (neurons, timesteps)</p> <p><strong><em>output</em></strong></p> <p>results obtained from <em>train_qgan.py</em> and <em>generate_fake_activity.py</em> </p> <p>contains:</p> <ul> <li><strong><em>losses</em></strong></li> </ul> <p>generator and critic loss for all training runs and steps</p> <ul> <li><strong><em>models</em></strong></li> </ul> <p>saved torch models every 10 training steps, for all training runs</p> <ul> <li><strong><em>generated_data</em></strong></li> </ul> <p>generated data for all models saved in <em>models</em></p>
Nonequilibrium steady-state thermoelectrics in Kondo-correlated quantum dots: Data
<p>Data sets generated using NRG-tDMRG for the manuscript titled "Nonequilibrium steady-state thermoelectric in the Kondo-correlated quantum dots".</p>
Investigating the fast spectral diffusion of a quantum emitter in hBN using resonant excitation and photon correlations
<p>Data from the article "Investigating the fast spectral diffusion of a quantum emitter in hBN using resonant excitation and photon correlations" (<a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.107.195304">Phys. Rev. B <strong>107</strong>, 195304 (2023)</a> – [<a href="https://arxiv.org/abs/2303.05315">arXiv</a>])</p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
Open the record for dataset details and reuse information.
Data and Code for "Space-time correlations in monitored kinetically constrained discrete-time quantum dynamics"
<div> <pre>This repository contains the source code, notebooks, data and basic figures for the manuscript <br>"Space-time correlations in monitored kinetically constrained discrete-time quantum dynamics". <br><br>Comments welcome. </pre> </div>
Numerical data: time dependence of spin-spin expectation value in "Nonmonotonic buildup of spin-singlet correlations in a double quantum dot" Phys. Rev. B 108, 144307 (2023).
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Data for article "Quantum Correlations of Light from a Room-Temperature Mechanical Oscillator"
<p>Figures data, data processing code and sample fabrication details for article "Quantum Correlations of Light from a Room-Temperature Mechanical Oscillator", </p> <p>Phys. Rev. X <strong>7</strong>, 031055 – Published 26 September 2017</p>
Data for "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement"
<p><span>This dataset is for the research article "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement".</span></p> <p><span> </span></p>
Theoretical analysis of correlations between two quantum fields exciting a three-level system using the cluster-expansion approach
<p>Dataset of the publication "Theoretical analysis of correlations between two quantum fields exciting a three-level system using the cluster-expansion approach" H. Rose, O. V. Tikhonova, T. Meier, and P. R. Sharapova, Proc. SPIE 11999, Ultrafast Phenomena and Nanophotonics XXVI, 1199905 (2022). ( https://doi.org/10.1117/12.2608528 ). The zip file includes the data on which the plots shown in figures 1, 2, and 3 are based.</p>
Coincidence measurements of two quantum-correlated photon pairs widely separated in the frequency domain
<p>Quantum correlation is a key concept characterizing the properties of quantum light sources and is important for developing quantum applications with superior performance. In particular, it enables photon pairs that are widely separated in the frequency domain, one in the visible region, the other in the infrared region, to be used for quantum infrared sensing without direct detection of infrared photons. Here, simultaneous multiwavelength and broadband phase matching in a nonlinear crystal could provide versatile photon-pairs source for broadband infrared quantum sensing. This paper describes direct generation and detection of two quantum-correlated photon pairs produced via simultaneous phase-matched processes in periodic crystals. These simultaneous photon pairs provide a correlated state with two frequency modes in a single pass. To confirm the correlation, we constructed an infrared-photon counting system with two repetition-synchronized fiber lasers. We performed coincidence measurements between two pairs, 980 nm and 3810 nm, and 1013 nm and 3390 nm, which yielded coincidence-to-accidental ratios of 6.2 and 6.5, respectively. We believe that our novel correlated light source with two separate pairs in the visible and infrared region complements a wide-range of multi-dimensional quantum infrared processing applications.</p>
Analysis of the nonlinear optical response of excitons in type-I and type-II quantum wells including many-body correlations
<p>Dataset of the publication “Analysis of the nonlinear optical response of excitons in type-I and type-II quantum wells including many-body correlations”, A. Trautmann, M. Stein, F. Schäfer, D. Anders, C. Ngo, J. T. Steiner, M. Reichelt, S. Chatterjee, and T. Meier, Proc. SPIE 12419, Ultrafast Phenomena and Nanophotonics XXVII, 124190A (2023) ( <a href="https://doi.org/10.1117/12.2650169">https://doi.org/10.1117/12.2650169</a> ). The zip file includes the data on which the plots are based.</p>
Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding
<p>Raw data of paper "<em>Ab initio</em> Quantum Simulation of Strongly Correlated Materials with Quantum Embedding".</p>
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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.