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62 results for “Quantum Computing”
A Modular Quantum Compilation Framework for Distributed Quantum Computing
<p>This repository contains the data used for the plots in "<em>A Modular Quantum Compilation Framework for Distributed Quantum Computing</em>" by D. Ferrari, S. Carretta and M. Amoretti.</p> <p>Data is located in the <em>'data'</em> directory in <em>.csv</em> format, a python script to generate the plots can be found in the main directory. The script was tested with <strong>python3.10</strong> and needs <strong>matplotlib</strong>, <strong>pandas</strong> and <strong>seaborn</strong> packages. Plots are saved as <em>.pdf</em> files in the <em>'figures'</em> directory.</p>
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack: Experimental Data
<p>Full experimental dataset for the publication "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack". The archive `application_motivated_benchmarks.zip` contains the following files and directories:</p> <p>- uncompiled_log.csv</p> <p>Gives IDs for the uncompiled circuits initially generated for use in our<br> experiments, along with the properties of the circuits.</p> <p>- properties_log.csv</p> <p>Gives IDs for device property files, along with the device and the time at which<br> they were collected.</p> <p>- compiled_log.csv</p> <p>Gives the calculated figures of merits for the compiled and run circuits.<br> Compiled circuits are identified by the ID of the uncompiled circuit, the<br> compilation strategy used, and the device compiled onto. Device property IDs at<br> the time of compilation and run are given.</p> <p>- circuits/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 2 forms.<br> <br> - uncompiled.qasm is the uncompiled circuit.<br> - files of the form 'strategy'_'device'.qasm are the compiled circuits.</p> <p>- data/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 3 forms.</p> <p> - prob_vector.csv contains the ideal output probability distribution.<br> - files of the form 'strategy'_'device'.csv contain the shot counts for<br> each compiled circuit when run on the real device.<br> - files of the form 'strategy'_'device'_simulated.csv contain the shot<br> counts for each compiled circuit when run using a classical simulator<br> with noise model build from device properties at the time of the real<br> run.</p> <p>- device_properties/</p> <p>Contains json files detailing device properties for each device property ID.</p> <p> </p>
Application-Oriented Performance Benchmarks for Quantum Computing
<p>Complete dataset and Jupyter Notebook used to produce image files for the paper at</p> <p> https://arxiv.org/abs/2110.03137.</p> <p>To execute the notebook, copy the .ipynb file and the _data directory to the top level of the repository at:</p> <p> https://github.com/SRI-International/QC-App-Oriented-Benchmarks</p> <p> </p>
Dataset: Quantum Computing Inc. (QUBT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging
Open the record for dataset details and reuse information.
Role of Quantum Computing in Shaping the Future of 6G Technology
<p>The dataset is provided for the data collected to understand the role of quantum cmputing in shaping the future of 6G technology. The dataset consist of all the raw data and analysed results with respect to the research questions.</p>
Data for Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
<p>Dataset substantiating the claims in <a href="https://arxiv.org/abs/2212.07957">[2212.07957] Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization (arxiv.org)</a></p>
Data from: Determining ground-state phase diagrams on quantum computers via a generalized application of adiabatic state preparation
<p>Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of competing states close to the level crossings. In this work, we use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. This methodology is illustrated by examining the ground states of the XY model with a magnetic field in the z-direction in one dimension. We are able to calculate an accurate phase diagram on both two and three site systems using IBM quantum machines.</p>
Code and data for N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling"
<p>Simulation code and data used in N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling."</p>
Supplemental data for "Estimating the Jones polynomial for Ising anyons on noisy quantum computers"
<p>This data supports "Estimating the Jones polynomial for Ising anyons on noisy quantum computers" by Chris N. Self, Sofyan Iblisdir, Gavin K. Brennen, and Konstantinos Meichanetzidis https://arxiv.org/abs/2210.11127</p> <p>Related code can be found in the GitHub repository: (https://github.com/chris-n-self/Ising-anyons-Jones-polynomials-for-NISQ). The 'analysis' folder here can be dropped inside the code repository in order to view the data using the 'view...' notebooks. The experimental data folders 'ibmq_...' contain the individual sets of results and can be used to generate new zero-noise-extrapolation fits.</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 Material - Uncovering the Effects of Quantum Computing on Software Engineering: A Systematic Mapping
<p>This is the supplementary material regarding: “On the Influence of Quantum Computing on Software Engineering: A Systematic Mapping”, the idea being to make it possible to reproduce the methodology applied in the Systematic Mapping (SM) used in the study. It consists of:</p> <ul> <li>Spreadsheet with the data of the papers selected in each step of the filtering phase of the SM development</li> <li>Fluxogram detailing the Methodology of the SM step-by-step<strong></strong></li> </ul> <h3><strong>Objectives</strong></h3> <p>The main objective of the SM was to provide a detailed analysis on how quantum computing has affected four software engineering topics: Software Testing and Quality; Software Reengineering/Modernization; Software Modeling and Software Processes and Development Platforms</p> <p>Based on the results of such Mapping, four research questions were defined realted to the quantum software engineering topic:</p> <ul> <li> <p>RQ1: <span>How has QC impacted software testing and qual</span><span>ity? This RQ aims to identify proposed methods for </span><span>testing quantum software and topics related to quantum </span><span>metrics and bugs.</span></p> </li> <li> <p>RQ2: How has QC influenced software reengineering and modernization practices? This study seeks to analyze the treatment of reverse engineering and refactorings within this context.</p> </li> <li> <p>RQ3: How is quantum software modeled? This research aims to investigate the impact of QC on modeling languages and the granularity level used.</p> </li> <li> <p>RQ4: How have development processes and platforms been shaped by QC? The goal is to assess the current maturity of processes and platforms for QC.</p> </li> </ul> <h3><strong>Preparation</strong></h3> <p>In this part we outline the resources, digital libraries, the search string and inclusion and exclusion criteria used in this SM on the influence of quantum computing on software engineering.</p> <p>The platform used to manage the articles and define the search string and inclusion and exclusion criteria was Parsifal. In this process articles were sourced from : ACM Digital; IEEE Digital Library; Science Direct and Scopus.</p> <p>A search string was employed in each digital library, adapted to the syntax of each of them, while encompassing the time frame from 01/2018 to 06/2023, with the Base Search String being:</p> <p><strong>(“Software Model” OR “Software Engineering” OR “Software Development” OR “Software Lifecycle” OR “Software Development Methodologies” OR “Software Project Management” OR “Testing” OR “Design Pattern” OR “Reengineering” OR “Reverse Engineering” OR “Metrics” OR “Service-Oriented”) AND (“Quantum Software” OR “Quantum Programming” OR “Quantum Computing” OR “Quantum Software Development” OR “Quantum Subroutine” OR “Quantum Program”)</strong></p> <p>Where the upper part includes terms related to software engineering and the lower part covers the quantum terms.</p> <p>Another important task was to define inclusion and exclusion criteria for filtering the studies, being them:</p> <p><strong>Inclusion Criteria</strong></p> <ul> <li> <p>The study is published in English.</p> </li> <li> <p>The content of the study is related to the topic being analyzed.</p> </li> </ul> <p><strong><strong>Exclusion Criteria</strong></strong></p> <ul> <li> <p>The study is not available</p> </li> <li> <p>The study is duplicated</p> </li> <li> <p>The study is not primary study (surveys, systematic reviews/mappings, talks, proceedings, etc)</p> </li> <li> <p>The content of the study is not related to the theme or it is too superficial.</p> </li> <li> <p>The content of the study was updated in a next more-complete version. When the authors published a new and more complete version of the same content, we remove the previous paper.</p> </li> </ul> <h3><strong>Conduction</strong></h3> <p>During the conduction of the papers filtering process there were four stages to which the set of papers were applied to until reaching the final set:</p> <ul> <li> <p>First stage: Removal of every paper that was duplicated, leaving only one instance of each paper.</p> </li> <li> <p>Second stage: Removal of papers that are not a primary study.</p> </li> <li> <p>Third stage: Removal of papers which keywords, title and abstract are not related to the topics of the Systematic Mapping;</p> </li> <li> <p>Fourth stage: Removal of papers whose content is not related to the topics of the Systematic Mapping.</p> </li> </ul>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
Cryogenic quantum computer control signal generation using high-electron-mobility transistors data
<p>Data generated for the publication "Cryogenic quantum computer control signal generation using high-electron-mobility transistors"</p>
Comparing LLM-Generated Tips and Expert-Created Tips in Quantum Computing Education
<p>This dataset includes the anonymized data from two studies with the goal to evaluate if LLM-generated tips can be used instead of expert-created tips to help students answer quantum computing questions. <br>For the main study (<em>main_test_anonymous.csv</em>) a between-subject design was used to quizz participants of the QUIKSTART 2024 summer school, giving them four multiple-choice quantum physics questions and one tip per question. Each participant was assigned one of four conditions represented by the combination of "creator" and "labeled_as" column in the csv file. In addition we asked to rate quality, correctness and helpfulness for each tip and to rate the perceived difficulty of the question. We removed demographics for anonymities sake.</p> <p>Additionally, we conducted a study directly comparing the LLM-generated and expert-created tips (<em>tip_eval_anonymous.csv</em>). Where we let experts and students rate the tips helpfulness, correctness, if they gave away the answer and if they pointed to relevant concepts. Furthermore, participants had to decide for each question which tip they preferred and were able to leave a comment to give their reasoning. We removed demographics for anonymities sake.<br><br>These datasets were evaluated in the paper "LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions" by Lars Krupp, Jonas Bley, Isacco Gobbi, Alexander Geng, Sabine Müller, Sungho Suh, Ali Moghiseh, Arcesio Castaneda Medina, Valeria Bartsch, Artur Widera, Herwig Ott, Paul Lukowicz, Jakob Karolus, Maximilian Kiefer-Emmanouilidis</p>
SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019,
<p>SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019. This compliments the data set at doi: 10.17863/CAM.30358 (copy these SPAWN files here into relevant folder at histograms_toc_figs_1_2/H2O_3_ccpVDZ/).</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>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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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.