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12 results for “quantum error correction”
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset
<p>Simulated sydromes measurement of quantum surface code error correction.<br>Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>".</p> <p>File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code></p> <p>Each file is formatted as csv with the following columns:</p> <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> <p>Only distance 3 is currently available with 10M samples for each physical fault rate.</p> <p>The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data
<p>Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"</p>
Data for "Optimization of decoder priors for accurate quantum error correction"
<p>Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.</p>
Data for "Quantum error correction below the surface code threshold"
<p>Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor.</p> <p>See README at the root of each zip archive for detailed description of each dataset.</p>
Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"
<p>Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory. See the README for more details.</p>
A heterometallic [LnLn'Ln] lanthanide complex as a qubit with embedded quantum error correction. Open data set
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1039/d0sc03107k</a> plus a table with information on how the data were obtained and processed.</p>
Demonstration of fault-tolerant Steane quantum error correction
<p>Source data underlying the graphical representations used in the figures and corresponding executed quantum circuits.</p>
Theoretical Design of Optimal Molecular Qudits for Quantum Error Correction. Open data set
<p>Data supporting the original figures 1, 2, 3 and 4 of the related publication.</p>
Characterizing quantum instruments: from non-demolition measurements to quantum error correction
<p>Source data underlying the graphical representations used in the figures.</p>
Data accompanying the publication "Quantum error correction with silicon spin qubits"
<p>Data accompanying the publication "Quantum error correction with silicon spin qubits"</p>
Quantum error correction with molecular spin qudits. Open dataset
<p>Data supporting the original figures 1, 3, 4, 5 of the related publication.</p>
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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.