Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
62
datasets available to search
ShareScore release 0.9.0
Dataset results
62 results for “Quantum Computing”
Measurement-free, scalable and fault-tolerant universal quantum computing
<p>The repository is supporting the publication "Measurement-free, scalable and fault-tolerant universal quantum computing". </p> <p>It includes the data shown in the manuscript as well as the simulation code and circuits that were used to obtain this data. </p>
Acceptance and Development of Quantum Computing in the Netherlands and Germany: Barriers and Remedies from a Multi-stakeholder Perspective}
<p>The repository contains the email sent to the candidates, the interview guide, the complete list of interview transcripts, the qualitative analysis performed with QDA Miner Lite, the code books of the initial and final data analysis, and the excerpts from the interviews.</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
<p> In this paper, we demonstrate a classical simulation framework for quantum computation by contracting tensor networks of sizes previously deemed out of reach. The main contribution of this work is a parallelization scheme called <em>index slicing</em> that breaks down an infeasibly large tensor network contraction task into smaller subtasks that can be executed fully in parallel, without interdependencies or intermediate communications. As a benchmarking example, we show that our algorithm can reduce the simulation of the Sycamore random circuit sampling task to less than 20 days, achieving an acceleration of over five orders of magnitude compared to the original proposal. We then showcase the capabilities of the simulation framework via investigations of near-term quantum algorithms and quantum error correction. Given the ubiquity of tensor networks in quantum information science, we believe that our simulation framework will be a valuable tool in the era of quantum information technology.</p>
Measuring the Capabilities of Quantum Computers
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="https://www.nature.com/articles/s41567-021-01409-7">Measuring the Capabilities of Quantum Computers</a>,</em> Nature Physics <strong>18</strong>, 75-79 (2022).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="http://https://doi.org/10.48550/arXiv.2112.09853">Scalable randomized benchmarking of quantum computers using mirror circuits</a>, </em>arXiv 2112.09853 (2021).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Learning a quantum computer's capability using convolutional neural networks
<p>This is supplemental data and code for: D. Hothem et al., <em>Learning a quantum computer's capability using convolutional neural networks, </em>(to be published).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Daniel Hothem (dhothem@sandia.gov).</p> <p>NOTE: This description template was borrowed from Timothy Proctor's Zenodo entry for: Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits.</p>
Data of "Thermodynamics of Quantum Trajectories on a Quantum Computer"
<p>The uploaded files contain the data of the simulations presented in the figures of the publication "Thermodynamics of Quantum Trajectories on a Quantum Computer".</p>
Hartree-Fock on a superconducting qubit quantum computer
Open the record for dataset details and reuse information.
Efficient parallelization of tensor network contractions for simulating quantum computation
Open the record for dataset details and reuse information.
Data from: Committing to quantum resistance: a slow defence for Bitcoin against a fast quantum computing attack
Quantum computers are expected to have a dramatic impact on numerous fields, due to their anticipated ability to solve classes of mathematical problems much more efficiently than their classical counterparts. This particularly applies to domains involving integer factorisation and discrete logarithms, such as public key cryptography. In this paper we consider the threats a quantum-capable adversary could impose on Bitcoin, which currently uses the Elliptic Curve Digital Signature Algorithm (ECDSA) to sign transactions. We then propose a simple but slow commit--delay--reveal protocol, which allows users to securely move their funds from old (non-quantum-resistant) outputs to those adhering to a quantum-resistant digital signature scheme. The transition protocol functions even if ECDSA has already been compromised. While our scheme requires modifications to the Bitcoin protocol, these can be implemented as a soft fork.
Multi-Gate FD-SOI Single Electron Transistor for hybrid SET-MOSFET quantum computing
Open the record for dataset details and reuse information.
Data for "Observation of the non-Hermitian skin effect and Fermi skin on a digital quantum computer"
Open the record for dataset details and reuse information.
Simulating Static and Dynamic Properties of Magnetic Molecules with Prototype Quantum Computers. Open data set
<p>Data supporting the original figures 1, 2, 3, 4, 5 of the related publication.</p>
Conveyor-mode single-electron shuttling in Si/SiGe for a scalable quantum computing architecture
<p>Dataset and Code for the Paper: Conveyor-mode single-electron shuttling in Si/SiGe for a scalable quantum computing architecture</p>
Demonstration of quantum volume 64 on a superconducting quantum computing system
<p>Data set published in 'Petar Jurcevic <em>et al</em> 2021 <em>Quantum Sci. Technol.</em> <strong>6</strong> 025020'</p>
Molecular nanomagnets with competing interactions as optimal units for qudit-based quantum computation. Open data set
<p>Data supporting the original figures 1, 3, 4, 5, 6 and 7 of the related publication.</p>
Data from: Committing to quantum resistance: a slow defence for Bitcoin against a fast quantum computing attack
Open the record for dataset details and reuse information.
Establishing trust in quantum computations
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="https://arxiv.org/abs/2204.07568">Establishing trust in quantum computations</a></em><em>, </em>arXiv 2204.07568 (2022).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The simulation code uses PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The `code_for_release` folder contains all code, and processed data required for reproducing the results presented in the paper. The `data_for_release` folder contains all raw data (this folder is ~70 Gb when uncompressed), which was generated using the code in `code_for_release`.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Supplementary data for "Resource-efficient photonic quantum computation with high-dimensional cluster states"
<p>Supplementary data for the paper "Resource-efficient photonic quantum computation with high-dimensional cluster states" by Ohad Lib and Yaron Bromberg.</p>
Dataset for An integrated microwave-to-optics interface for scalable quantum computing
<p>Source data for figures.</p>
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.