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37 results for “quantum measurement”
Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"
<p>Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", <em>npj Quantum Information</em><strong> 3</strong>, Article number: 16 (2017).</p>
Data for "An autonomous quantum machine to measure the thermodynamic arrow of time"
<p>Numerical simulation data from the article "An autonomous quantum machine to measure the thermodynamic arrow of time"</p> <p>J. Monsel, C. Elouard, A. Auffèves <em>npj Quantum Inf</em> <strong>4</strong>, 59 (2018). <a href="https://doi.org/10.1038/s41534-018-0109-8" target="_blank" rel="noopener">https://doi.org/10.1038/s41534-018-0109-8</a></p> <p>See the jupyter notebook for the data analysis and figures.</p> <p>The code to perform the numerical simulations is given in the repository <a href="https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system" target="_blank" rel="noopener">https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system</a>.</p>
Supporting data for "Measurable fractional spin for quantum Hall quasiparticles on the disk"
<p>Supporting data for the manuscript "Measurable fractional spin for quantum Hall quasiparticles on the disk", by T. Comparin, A. Opler, E. Macaluso, A. Biella, A. P. Polychronakos, L. Mazza.<br> If you use these numerical results in a scientific work, please cite the corresponding article [<a href="https://link.aps.org/doi/10.1103/PhysRevB.105.085125">Phys. Rev. B <strong>105</strong>, 085125 (2022)</a>].<br> For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr).</p> <p>We computed the density profile rho(r) for the Laughlin state (with filling 1/m, for m=2,3,4) and for the Halperin 221 state, by means of Monte Carlo simulations. All data are in units of the magnetic length (that is, with lB=1).<br> When present, labels "QH0", "QH1" and "QH2" in the filenames correspond to the case with q=0, q=1 or q=2 quasiholes localized at the origin.</p> <p><br> Folders:</p> <ul> <li>Data_Laughlin contains the Laughlin density-profile data used to compute the spin values in Fig. 1.</li> <li>Data_Halperin221 contains the Halperin 221 density-profile data used to compute the spin values in Fig. 2. Filenames include a label for the type and number of quasiholes: "A" stands for Gamma=A and q=1; "AA" stands for Gamma=A and q=2; "AB" stands for Gamma=AB and q=1; "AABB" stands for Gamma=AB and q=2.</li> <li>Data_Laughlin_boundary contains the Laughlin density-profile data shown in Fig. 6.</li> <li>Data_Halperin221_boundary_A contains the Laughlin density-profile data shown in Fig. 7, for Gamma=A.</li> <li>Data_Halperin221_boundary_AB contains the Laughlin density-profile data shown in Fig. 7, for Gamma=AB.</li> </ul>
Data Analysis files for "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity"
<p>Data Analysis for the paper "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity", which is published in PRX Quantum <strong>3</strong>, 020309 (2022).</p>
Data for "Challenges of variational quantum optimization with measurement shot noise"
<p>In this repository, we save all the data used in the article <em>"Challenges of variational quantum optimization with measurement shot noise"</em> <a href="https://doi.org/10.1103/PhysRevA.109.032408" target="_blank" rel="noopener">Phys. Rev. A 109, 032408</a>.</p> <p>For reproducing the same figure as in the article, we provide an interactive notebook 'paper-figure.ipynb' and its HTML version. </p> <p><strong>Data</strong></p> <p>Unzip with</p> <pre><code>tar -xvf data.tar.gz</code></pre> <p>You will find ten data files:</p> <ul> <li> `dataferro.csv` Ferromagnetic model both for VQE and QAOA with random parameters initialization;</li> <li>`datanoise.csv`Ferromagnetic model for VQE with random parameters initialization and circuit noise;</li> <li>`data_spinglass.csv` Disordered model both for VQE and QAOA with random parameters initialization;</li> <li>`data_linopt.csv` Ferromagnetic model for QAOA with annealing-like parameters initialization;</li> <li>`data_lininit_rand.csv` Disordered model for QAOA with annealing-like parameters initialization;</li> <li>`data_spinglass_iter1.csv` Disordered model for QAOA, no optimization and random parameters initialization;</li> <li>`data_opt16shots.csv` Ferromagnetic model for QAOA, no optimization and annealing-like parameters initialization;</li> <li>`datagrad.csv` Ferromagnetic model for VQE with gradient-based optimization;</li> <li>`datagrad_eps05.csv` Ferromagnetic model for QAOA with gradient-based optimization;</li> <li>`grad_epsilon.npz` Ferromagnetic model for QAOA with gradient-based optimization, varying \(\varepsilon\) in the finite differences formula.</li> </ul> <p>Most of the data are CSV sheets, where you can find the aggregate results of the simulations described in our article. </p> <p><strong>Abstract</strong></p> <p>Quantum enhanced optimization of classical cost functions is a central theme of quantum computing due to its high potential value in science and technology.<br>The variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are popular variational approaches that are considered the most viable solutions in the noisy-intermediate scale quantum (NISQ) era.<br>Here, we study the scaling of the quantum resources, defined as the required number of circuit repetitions, to reach a fixed success probability as the problem size increases, focusing on the role played by measurement shot noise, which is unavoidable in realistic implementations.<br>Simple and reproducible problem instances are addressed, namely, the ferromagnetic and disordered Ising chains. <br>Our results show that: </p> <ol> <li>VQE with the standard heuristic ansatz scales comparably to direct brute-force search when energy-based optimizers are employed. The performance improves at most quadratically using a gradient-based optimizer;</li> <li>When the parameters are optimized from random guesses, also the scaling of QAOA implies problematically long absolute runtimes for large problem sizes;</li> <li>QAOA becomes practical when supplemented with a physically-inspired initialization of the parameters.</li> </ol> <p>Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop, but focus on smart parameters initialization.</p>
Measured data of article "Superconducting NbN–Al hybrid technology for quantum devices"
<p>The folder contains raw data of figure 3 & 4 as well as a preprint of the article:</p> <p><em>Superconducting NbN–Al hybrid technology for quantum devices</em></p> <p>Authors: E. Mutsenik, S. Linzen, E. Il’ichev, M. Schmelz, M. Ziegler, V. Ripka, B. Steinbach, G. Oelsner, U. Hübner, and R. Stolz</p> <p>Journal: Low Temperature Physics/Fizyka Nyzkykh Temperatur, 2023, Vol. 49, No. 1, pp. 98–101</p>
Data and fitting script for "Direct measurement of a sin(2φ) current phase relation in a graphene superconducting quantum interference device"
<p>This repository contains data and Python analysis scripts used for the publication "Direct measurement of a sin(2\phi) current phase relation in a graphene superconducting quantum interference device (https://doi.org/10.48550/arXiv.2405.13642).</p> <p>The repository is organized as follows: the raw data are encapsulated in a QCoDes database (https://microsoft.github.io/Qcodes/) named 'D-SQUID-06.db'. Post-treated critical current data are included as .csv files and are indexed by measurement ids.</p> <p>The principal analysis is realized in the Jupyter notebook 'Fits_and_Figures.ipynb', which includes all article figures as well as fit functions for fitting both critical currents Ic- and Ic+ simulatenously, first using analytical expression from equation 3 then using numerical expression from equation 5. All fits mentionned in the article are performed there. The repository includes a generic notebook "Extract_Critical_Current.ipynb" used to explore the raw data in the Qcodes database and features an enhanced peak detection script that we used to automatically extract critical current data despite having some artifacts on raw differential conductance versus bias current and magnetic field.</p> <p>We acknowledge the contribution of R. Kerjouan for developing the Python fitting script.</p>
Data for: Analysis of Conformational Exchange Processes using Methyl-TROSY-Based Hahn Echo Measurements of Quadruple-Quantum Relaxation
<p>Raw experimental data used in associated publication. A full list of experiments is provided in the README.md file.</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 and code associated with the paper 'Measurement-induced collective vibrational quantum coherence under spontaneous Raman scattering in a liquid'
<p>Data and code associated with the following paper <a href="https://doi.org/10.1038/s41467-023-38483-9">V. Vento, S. Tarrago-Velez et al., Nat. Commun. (2023)</a></p> <p>A thorough explanation of the experiment performed is available there.</p> <p>The name of each sub-folder and file in <strong>CS2_data_code.zip</strong> indicates the corresponding figure number ("FIG #") and the type of content ("Data", "Analysis" or "Model").</p> <p> </p>
Data and Codes for "Non-local transport measurements in hybrid quantum Hall - superconducting devices"
<p>Datasets and processing code for reproduction of "Non-local transport measurements in hybrid quantum Hall - superconducting devices".</p>
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 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 and analysis for the paper "Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots"
<p>This repository contains the raw data and analysis code used to generate the figures in the manuscript <em>Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots</em>. </p> <p><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.064503">Link to publication</a></p> <p><a href="https://arxiv.org/abs/2110.05373">Link to arXiv</a></p>
Replication package for our paper entitled "Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements"
<p><br> -Experiment1<br> Contains the source files for experiment 1 and the study subjects.</p> <p> -Experiment2<br> Contains the source files for experiment 2 and a mutation generator.</p> <p> -results<br> Contains the results from experiment 1 and 2 and postprocessing files</p> <p> -RQ1,RQ2,RQ3<br> Contains source files for figures, tables and data for each research question<br> of the paper</p> <p><br>Please inspect the README.txt file for more details.</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>
Supporting data for "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer"
<p>This repository contains the supporting data for the publication: "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer".</p>
Direct measurement of the quantum metric tensor in solids
Open the record for dataset details and reuse information.
Supporting Data and Code for "Topological Order from Measurements and Feed-Forward on a Trapped Ion Quantum Computer"
<p>Supporting Data and Code for the paper "Topological Order from Measurements and Feed-Forward on a Trapped Ion Quantum Computer". </p> <p>Navigate to wen16/ then pip install -r requirements.txt<br>To generate all the plots/data run plots_and_data.ipynb</p>
Characterizing quantum instruments: from non-demolition measurements to quantum error correction
<p>Source data underlying the graphical representations used in the figures.</p>
ScienceDex guides
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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.