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20 results for “quantum materials”
Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"
<p>This is a file collection as supplementary material for the paper <em>Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>.</em> It consists of:</p> <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> <p>The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in <em>Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a></em>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.</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>
Data of paper Chemically vapor deposited Eu3+:Y2O3 thin films as a material platform for quantum technologies
<p>Data of the figures of the paper:</p> <p>N. Harada, A. Ferrier, D. Serrano, M. Persechino, E. Briand, R. Bachelet, I. Vickridge, J.-J. Ganem, P. Goldner, and A. Tallaire, <em>Chemically Vapor Deposited Eu 3+:Y 2O 3thin Films as a Material Platform for Quantum Technologies</em>, J. Appl. Phys. <strong>128</strong>, 055304 (2020). doi: <a href="https://doi.org/10.1063/5.0010833">10.1063/5.0010833</a></p> <p> </p>
Exceptional electronic transport and quantum oscillations in thin bismuth crystals grown inside van der Waals materials
<p>Confining materials to two-dimensional forms changes the behavior of electrons and enables new devices. However, most materials are challenging to produce as uniform thin crystals. Here, we present a synthesis approach where thin crystals are grown in a nanoscale mold defined by atomically-flat van der Waals (vdW) materials. By heating and compressing bismuth in a vdW-mold made of hexagonal boron nitride (hBN), we grow ultraflat bismuth crystals less than 10 nanometers thick. Due to quantum confinement, the bismuth bulk states are gapped, isolating intrinsic Rashba surface states for transport studies. The vdW-molded bismuth shows exceptional electronic transport, enabling the observation of Shubnikov–de Haas quantum oscillations originating from the (111) surface state Landau levels. By measuring the gate-dependent magnetoresistance, we observe multi-carrier quantum oscillations and Landau level splitting, with features originating from both the top and bottom surfaces. Our vdW-mold growth technique establishes a platform for electronic studies and control of bismuth’s Rashba surface states and topological boundary modes. Beyond bismuth, the vdW-molding approach provides a low-cost way to synthesize ultrathin crystals and directly integrate them into a vdW heterostructure. </p>
Dataset for "Engineering defect clustering in diamond-based materials for technological applications via quantum mechanical descriptors"
<p>The unique set of extreme physical properties makes diamond an ideal candidate for applications in the energy industry such as in high-power and high-frequency electronics as well as in electrochemistry and photovoltaics. Furthermore, dopant-vacancy complexes in diamond can be exploited for further development of quantum computers, single-photon emitters, high-precision magnetic field sensing and nanophotonic devices. While certain dopant-vacancy complexes are well-studied, studies of other dopant/vacancy clusters are focused mostly on defect detection while investigations on how to tune their electronic and optical properties for specific applications is mostly omitted. To this aim, we attempted to reveal coupled structural-electronic features and their effect on the band gap of such defects through first principle calculations. We investigated four different defect types: a) dopant-vacancy complexes (X-V), b) two dopants as nearest neighbours (X-X), c) two dopants separated by one carbon atom (X-C-X) and d) two dopants separated by a vacancy (X-V-X). For each of these configurations, we considered Al, B, N, P and Si as dopant atoms. This dataset contains input files needed to reproduce every ground state geometry used in our study.</p>
Data of the publication: Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing
<p>Data corresponding to the figures of the publication "Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing" by N. Kunkel and Ph. Goldner (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1)
<p>This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of JSON files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database. </p><p>Here, we also provide the larger computational data JSON files for 700 compounds. This file consists of all important LOBSTER computation output file data stored as a dictionary.</p><p>Rest 820 computational data JSONs are provided as part of the following repository: A Quantum-Chemical Bonding Database for Solid-State Materials (Part 2): <a href="https://doi.org/10.5281/zenodo.8092187">https://doi.org/10.5281/zenodo.8092187.</a> </p><p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a> Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Additionally, all the scripts and tools used for curating (including metadata description), benchmarking, and reusing our data are documented in the openly accessible repository. This enables one to reproduce the data and results presented in our work fully. These scripts can be accessed from either of the following links:</p><p>Zenodo: <a href="https://doi.org/10.5281/zenodo.8172527">https://doi.org/10.5281/zenodo.8172527</a></p><p>Github: <a href="https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/tree/v1.0.6">https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/ (v1.0.6)</a></p><p>The dataset will also be available through <a href="https://next-gen.materialsproject.org/">The Materials Project</a> soon.</p>
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 2)
<p>This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database. </p><p>Here, we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output file data stored as a dictionary.</p><p>This dataset is published as part of our publication: <a href="https://www.nature.com/articles/s41597-023-02477-5">A Quantum-Chemical Bonding Database for Solid-State Materials. </a>Details about the data generation, validation, and metadata description can be found in our publication.</p><p>Additionally, all the scripts and tools used for curating (including metadata description), benchmarking, and reusing our data are documented in the openly accessible repository. This enables one to reproduce the data and results presented in our work fully. These scripts can be accessed from either of the following links:</p><p>Zenodo: <a href="https://doi.org/10.5281/zenodo.8172527">https://doi.org/10.5281/zenodo.8172527</a></p><p>Github: <a href="https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/tree/v1.0.6">https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/ (v1.0.6)</a></p><p>The dataset will also be available through <a href="https://next-gen.materialsproject.org/">The Materials Project</a> soon.</p>
Quantum Material-Based Self-Propelled Microrobots for the Optical "On-the-Fly" Monitoring of DNA
<p>Quantum dot-based materials have been found to be excellent platforms for biosensing and bioimaging applications. Herein, self-propelled microrobots made of graphene quantum dots (GQD–MRs) have been synthesized and explored as unconventional dynamic biocarriers toward the optical “on-the-fly” monitoring of DNA. As a first demonstration of applicability, GQD–MRs have been first biofunctionalized with a DNA biomarker (i.e., fluorescein amidite-labeled, FAM-L) via hydrophobic π-stacking interactions and subsequently exposed toward different concentrations of a DNA target. The biomarker–target hybridization process leads to a biomarker release from the GQD–MR surface, resulting in a linear alteration in the fluorescence intensity of the dynamic biocarrier at the nM range (1–100 nM, <em>R</em><sup>2</sup> = 0.99), also demonstrating excellent selectivity and sensitivity, with a detection limit as low as 0.05 nM. Consequently, the developed dynamic biocarriers, which combine the appealing features of GQDs (e.g., water solubility, fluorescent activity, and supramolecular π-stacking interactions) with the autonomous mobility of MRs, present themselves as potential autonomous micromachines to be exploited as highly efficient and sensitive “on-the-fly” biosensing systems. This method is general and can be simply customized by tailoring the biomarker anchored to the GQD–MR’s surface.</p>
Prospects for Quantum Software Engineering in the Next Decade - Supplementary material
<p>This supplementary material corresponds to the study "<em>Prospects for Quantum Software Engineering in the Next Decade</em>" for the Software Engineering in 2030 Workshop (SE2030).</p> <p>In this supplementary material you will find the papers found in the search for the terms "<em>Quantum Software Engineering</em>" in the bibliographic databases of Scopus and Google Scholar, as well as their evolution since 2004.</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>
Quantum Software Engineering: Roadmap and Challenges Ahead - Supplementary material
<p>This supplementary material corresponds to the study "<em>Quantum Software Engineering: Roadmap and Challenges Ahead</em>" for the Special Issue Roadmap to 2030 by the ACM Transactions on Software Engineering and Methodology (TOSEM).</p> <p>In this supplementary material you will find the papers found in the search for the terms "<em>Quantum Software Engineering</em>" in the bibliographic databases of Scopus and Google Scholar, as well as their evolution since 2004.</p>
Supplementary material 1 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
The attachment contains the original reports given to the FWF by the reviewers of the grant application, published here with their permission.
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
<h2>README</h2> <p>The dataset consists of the following components:<br> <br>- `<strong>prompts.txt</strong>`: This file contains the prompts used for large language models.<br> <br>- `<strong>Dataset</strong>` directory: includes general information about the dataset. Specifically, the `dataset.xlsx` file lists flaky and non-flaky tests, along with their root causes and fix types.<br> <br>- `<strong>Full</strong>` directory contains two subdirectories: `Flaky` and `Non-flaky`. Each of these directories is organized by individual GitHub organization projects, with each project having its list of repository subdirectories. These subdirectories are further divided into “issues” and “pull requests” (PRs).</p> <p><br>- `<strong>Method</strong>` level subdirectory has a similar structure but contains extracted code snippets at the method level instead of full code listings. The `code.diff` file is copied over and left unaltered. </p> <p><br>- <strong>Issue Directories (IRs):</strong> Named with an `issueID` template, each issue directory contains a `log.issue` file that includes the extracted description, comments, and metadata.<br> <br>- <strong>PR Directories (PRs)</strong>: Named using the `prID` template, each PR directory contains the text, comments, and metadata in the `pr.log` file. The text of the associated issue is stored in the `log.issue` file. Code listings are stored in a file with the `.bug` suffix, while the corresponding fixed version is in a `.fix` file. The `code.diff` file contains the patch that transforms the `.bug` version into the `.fix` version.</p> <p><br><strong>Additional notes:</strong><br>Issues with associated pull requests in `dataset.xlsx` are combined into the pull request directory template. If two pull requests are listed for a row, a PR directory is created for each. Due to updates in the extended dataset, some repositories have been renamed or archived, meaning the current repository directory names in `Dataset` will include both the previous and new names if it has been changed (e.g., a repository previously saved as Qiskit/qiskit-terra may now be saved as Qiskit/qiskit following the renaming from qiskit-terra to qiskit).</p> <h2>Directory Structure:</h2> <p><br>├── prompts.txt<br>├── Dataset/<br> └── dataset.xlsx<br>├── Full/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-Flaky/<br> └── <Organization>/<Repository>/...<br>├── Method/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-flaky/<br> └── <Organization>/<Repository>/...</p> <p> </p>
Data for "Ab initio quantum-mechanical predictions of semiconducting photocathode materials"
<p>Input and output files of the calculations presented in the publication <em>"Ab initio quantum-mechanical predictions of semiconducting photocathode materials"</em>.</p> <p>The zip-archives contain the data relevant for subsections <em>3.1 Electronic structure</em>, <em>3.2 Optical Spectroscopy</em> and <em>3.3. Core-level Spectroscopy. </em>The aiida-archives (suffix <em>".aiida"</em>) contain the calculation and provenance details for the high-throughput workflow described in subsection <em>3.4 High-throughput material screening</em>.<br> </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>
Supplementary material 1 from: Todorova N, Rangelov M, Dincheva I, Badjakov I, Enchev V, Markova N (2022) Potential of hydroxybenzoic acids from Graptopetalum paraguayense for inhibiting of herpes simplex virus DNA polymerase – metabolome profiling, molecular docking and quantum-chemical analysis. Pharmacia 69(1): 113-123. https://doi.org/10.3897/pharmacia.69.e79467
Tables S1–S3 and Figures S1–S4
Data for figures: Super-resolution lightwave tomography of electronic bands in quantum materials
<p>These datasets are supplement to the publication of the same title. Explanation on how to read the files is provided in the file "readme.pdf".</p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
Open the record for dataset details and reuse information.
Supplementary Material - Dataset for "Automating Quantum Program Maintenance: Flakiness Detection and Resolution"
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.