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56 results for “Quantum simulation”
Numerical dataset for quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback
<p>The data consist of numerical results obtained from quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback.</p> <p>These data are used in the preprint titled <em>"Heat current and fluctuations between a dissipative qubit and a monitor under continuous measurement and feedback."</em></p> <p>We performed numerical simulations of the stochastic master equation using the supercomputer at ISSP.</p> <p>The contents are as follows:</p> <ul> <li>Numerical results for the correlation functions (<code>F*.dat</code>).</li> <li>Numerical results for the Fano factor and the power spectrum (<code>fano_factor*.dat</code>).</li> <li>Python code to calculate the Fano factor and the power spectrum from the correlation functions (<code>fano_factor_v6.py</code>).</li> <li>Figures illustrating the Fano factor and the power spectrum (<code>fig*.eps</code>).</li> </ul>
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>
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>
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>
Towards quantum utility for NMR quantum simulation on a NISQ computer
<p>Included are CSV files to reproduce the plots from the publication "Towards quantum utility for NMR quantum simulation on a NISQ computer" and a jupyter notebook script for plotting. If all files are saved in the same folder it should work immediately. Plotly library is used for plotting.</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>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
Simulation results for "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes"
<p>This dataset contains simulations results presented in the paper "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes".</p> <p>The files are in `csv` file format, with data easily processable using the python library `sinter`.</p>
Simulation data and source code for Hausdorff dimension measurements in two-dimensional quantum gravity
<p>This entry contains the source code and simulation data used as basis for the paper</p> <p>J. Barkley, T. Budd, "Precision measurements of Hausdorff dimensions in two-dimensional quantum gravity." Preprint <a href="https://arxiv.org/abs/1908.09469">arXiv:1908.09469</a> (2019)</p> <p>Both the source code and the data consist of two parts:</p> <ul> <li>Measurements of (dual) graph distances in various models of random planar maps.</li> <li>Measurements of discrete Liouville first passage percolation distances on a regular lattice with periodic boundary conditions.</li> </ul> <p>Instructions on compiling and running the simulation software are included with the source code (see README files). Descriptions of the simulation data formats accompany the data files (see README files again). For background on the simulation and data analysis we refer to the publication mentioned above.</p>
Datasets, figures and simulation scripts for "Quantum circuit compilation with quantum computers"
<p>The files contain the datasets and figures with the results of the manuscript "<a title="Quantum circuit compilation with quantum computers" href="https://doi.org/10.48550/arXiv.2408.00077" target="_blank" rel="noopener">Quantum circuit compilation with quantum computers</a>".</p> <p>The repository URL links to the repository with the simulation scripts used to produce the datasets and figures.</p>
Simulation data and figures for "Benchmarking Quantum Red TEA on CPUs, GPUs, and TPUs"
<p>The data set and figures is part of "Benchmarking Quantum Red TEA on CPUs, GPUs, and TPUs" available at <a title="arXiv2409.03818" href="https://doi.org/10.48550/arXiv.2409.03818" target="_blank" rel="noopener">arXiv 2409.03818</a>. It contains the simulation results from Leonardo (Cineca) and the TPUs. The plotting script is part of the qredtea package.</p>
Trapped-Ion Quantum Simulation of Electron Transfer Models with Tunable Dissipation
<p>This package includes the data, the theory, and the source code to plot both in Mathematica to reproduce the figures of the paper.</p>
Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system
<p>Dataset of the publication “Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system“, H. Rose, J. Paul, J. K. Wahlstrand, A. Bristow, and T. Meier, Proceedings of the SPIE 11684, 1168414 (2021) ( <a href="https://doi.org/10.1117/12.2576696">https://doi.org/10.1117/12.2576696</a> ). The zip file includes the data on which the plots shown in figure 2 are based.</p>
Relative Phase Data to 'Experimental observation of curved light-cones in a quantum field simulator', arXiv:2209.09132
<p><strong>Relative phase profiles and averaged density profiles for the results shown in arXiv:2209.09132</strong></p> <p>Each file "phase_and_mean_density_scan_X.mat" contains data for a measurement presented in the manuscript, where "X" is the corresponding scan number.<br> The following table shows the relevant scan number to measurement descriptions mentioned in the manuscript (see Table S1 in the SI Appendix).</p> <table align="center"> <thead> <tr> <th scope="col">Measurement description</th> <th scope="col">Scan number</th> </tr> </thead> <tbody> <tr> <td> <p> Homogeneous (main text)</p> </td> <td> 9185</td> </tr> <tr> <td> <p> Inhomogeneous with sharp edges </p> </td> <td> 10419</td> </tr> <tr> <td> <p> Inhomogeneous with smoothed edges</p> </td> <td> 8935</td> </tr> <tr> <td> <p> Homogeneous 2 (SI Appendix)</p> </td> <td> 10455</td> </tr> </tbody> </table> <p> </p> <p><strong>File Contents</strong></p> <p>Each file contains the following variables:</p> <ul> <li>"phase": A MATLAB cell containing all the phase profiles for every time step. Thus, "phase{t_ind}" is a matrix where rows represent experimental realizations and columns the spatial grid points. For example, "phase{5}(1,:)" would be a one-dimensional phase profile, representing the first realization of the fifth time step. To learn more about the extraction of phase profiles, read Section 2 and see Fig. S5 in SI Appendix.</li> <li>"z_grid_phase_si": Vector. Grid points for phase profiles in SI units (m).</li> <li>"averaged_density_si": Vector. Averaged initial linear density in SI units (m^-1). See Fig. 1(a).</li> <li>"z_grid_density_si": Vector. Grid points for averaged density in SI units (m).</li> <li>"times_si": Vector. Time points in SI units (s).</li> </ul> <p> </p> <p><strong>Matlab script calculating the velocity field</strong></p> <p>In addition to the data, a MATLAB script (velocity_field_calculation.m) loads a data file and calculates the velocity field and its correlations following the equations in the manuscript:</p> <ul> <li>"u": MATLAB cell. Velocity field for every time step.</li> <li>"u_u_corr": MATLAB cell. Second-order correlations of the velocity field for every time step.</li> <li>"std_u_u_corr": MATLAB cell. Standard deviation of second-order correlations of the velocity field for every time-step.</li> </ul> <p>Finally, the script plots "u_u_corr" for all the time steps and plots the averaged linear density.</p>
Simulation of free fermion transport on 2D lattice using constant-depth quantum circuits
<p>In these simulations, we consider a fermion initialized on a reference lattice site, and observe how it evolves freely through a two-dimensional (2D) lattice. The lattice comprises 16 sites with closed boundary conditions, and lattice site '0' is considered our reference site, where the fermion is initialized. We examine transport of a fermion on this 16-site lattice both with and without disorder. To do this, we track the occupation number at varying distances $M$ from the reference site as the fermion evolves freely through time. When there is no disorder in the system, we expect the fermion to behave ballistically and oscillate back and forth within the lattice. The file '4x4_2DFF_mu=0.0_nsteps=900_t=90.0_backend=ibm_washington_shot=50000_nis_ps_DD_M3.txt' has the results from simulating a free fermion on a 2D lattice with no disorder on the ibmq_washington QPU, while '4x4_2DFF_mu=0.0_nsteps=900_t=90.0_backend=qasm_sim_shot=100000_nis.txt' has the results from the noise-free quantum simulator. </p> <p>When there is large random disorder in the system, we expect the fermion to exhibit Anderson localization. '4x4_2DFF_mu=10.0_nsteps=900_t=90.0_backend=ibm_washington_shot=50000_nis_ps_DD_M3_Anderson_loc.txt' shows results from simulating a free fermion on a 2D lattice with large random disorder on the ibmq\_washington QPU, while '4x4_2DFF_mu=10.0_nsteps=900_t=90.0_backend=qasm_sim_shot=100000_nis.txt' shows results from the noise-free quantum simulator. </p> <p>Simulations on the the noise-free quantum simulator were performed with 100,000 shots. Simulations on the QPU were performed with 50,000 shots and any shot that did not conserve particle number was discarded. Two straightforward error mitigation techniques were also used to reduce noise in the results from the QPU. The first was a scalable readout error mitigation method implemented with the mthree package, which reduces errors in quantum measurement via calibration. The second was dynamical decoupling, a method that can suppress qubit decoherence via the application of a set of pulses (which together amount to application of the identity operator) to idling qubits which cancels the system-environment interaction.</p>
Quantum turbulence in superfluid Fermi gas: results of numerical simulation
<p>Raw data of numerical simulation of quantum turbulence in superfluids Fermi gas.<br>Datasets are raw data used for publication: <br><em>Fermionic quantum turbulence: Pushing the limits of high-performance computing</em><br>by Gabriel Wlazłowski, Michael McNeil Forbes, Saptarshi Sarkar, Andreas Marek, and Maciej Szpindler<br><a href="https://doi.org/10.1093/pnasnexus/pgae160"><em>PNAS Nexus</em>, Volume 3, Issue 5, May 2024, pgae160</a></p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
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Integrals for "Quantum Simulations of Chemistry in First Quantization with any Basis Set"
<p>Integrals used to study scalings and carry out resource estimations. We did not upload the matrices for basis sets with 32000 functions because of large matrix sizes (8 GB per matrix). These integrals correspond to matrix elements of the Hamiltonian in the original basis (Eq. (II.1) and Eq.(III.6)) in this version of the manuscript: (<a href="https://arxiv.org/abs/2408.03145v2">arXiv:2408.03145v2</a> [quant-ph]), and not in the Pauli string representation. </p> <p>UPDATE: in the previous version of matrices in dual plane wave basis, kinetic energy + electron nuclear term gave the correct one-body contribution, but each individual file had an error in it. In this version, we updated the correct kinetic energy and electron nuclear matrices. Notice that electron-nuclear files are just diagonal elements so you need to convert it to a matrix to get correct kinetic + electron nuclear term. Also, dual plane waves electron-electron matrix includes a factor of 1/2 in it. FCIDUMP files are the same as in previous version.</p>
Data and plotting code for 'Many-body-localized discrete time crystal with a programmable spin-based quantum simulator'
<p>Underlying data and plotting for the publication: "Many-body-localized discrete time crystal with a programmable spin-based quantum simulator", J. Randall et al., 2021.</p> <p>This directory contains:</p> <p>- “Data_analysis.ipynb”: The master Jupyter notebook from which all plots produced in the main text and supplementary materials can be reproduced.<br> - “Data.zip”: A zipped folder containing the json files as loaded within “Data_analysis.ipynb”. These files contain the underlying x, y, and, where appropriate, y error values for each corresponding plot.</p> <p>The notebook should be executed using python3.</p>
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Allen Brain Atlas
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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.