Nanopublications LDF server

Matches in Nanopublications for { <arcp://name,uts_public_data_repo/70967290a59911eca9a84993133e6628> ?p ?o ?g. }

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• 70967290a59911eca9a84993133e6628 name "QDataSet: Quantum Datasets for Machine Learning" assertion.
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• 70967290a59911eca9a84993133e6628 description "The QDataSet comprises 52 datasets based on simulations of one- and two-qubit systems evolving in the presence and/or absence of noise subject to a variety of controls. It has been developed to provide a large-scale set of datasets for the training, benchmarking and competitive development of classical and quantum algorithms for common tasks in quantum sciences, including quantum control, quantum tomography and noise spectroscopy. It has been generated using customised code drawing upon base-level Python packages in order to facilitate interoperability and portability across common machine learning and quantum programming platforms. Each dataset consists of 10,000 samples which in turn comprise a range of data relevant to the training of machine learning algorithms for solving optimisation problems. The data includes a range of information (stored in list, matrix or tensor format) regarding quantum systems and their evolution, such as: quantum state vectors, drift and control Hamiltonians and unitaries, Pauli measurement distributions, time series data, pulse sequence data for square and Gaussian pulses and noise and distortion data. The total compressed size of the QDataSet (using Pickle and zip formats) is around 14TB (uncompressed, around 100TB). Researchers can use the QDataSet in a variety of ways to design algorithms for solving problems in quantum control, quantum tomography and quantum circuit synthesis, together with algorithms focused on classifying or simulating such data. We also provide working examples of how to use the QDataSet in practice and its use in benchmarking certain algorithms. The associated paper provides in-depth detail on the QDataSet for researchers who may be unfamiliar with quantum computing, together with specifications for domain experts within quantum engineering, quantum computation and quantum machine learning. The dataset details are set out in the link to raw files. " assertion.
• 70967290a59911eca9a84993133e6628 citation 2108.06661 assertion.
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• 70967290a59911eca9a84993133e6628 creator Christopher.Ferrie@uts.edu.au assertion.
• 70967290a59911eca9a84993133e6628 creator akram.youssry@eng.asu.edu.eg assertion.
• 70967290a59911eca9a84993133e6628 creator dacheng.tao@sydney.edu.au assertion.
• 70967290a59911eca9a84993133e6628 creator elija.t.perrier@student.uts.edu.au assertion.
• 70967290a59911eca9a84993133e6628 datePublished "2022-03-28T04:39:03.553Z" assertion.
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• 70967290a59911eca9a84993133e6628 license ""_:license/http://creativecommons.org/licenses/by/3.0/au"" assertion.
• 70967290a59911eca9a84993133e6628 publisher www.uts.edu.au assertion.
• 70967290a59911eca9a84993133e6628 dateCreated "2022-03-28T04:39:03.553Z" assertion.
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• 70967290a59911eca9a84993133e6628 keywords "machine learning" assertion.
• 70967290a59911eca9a84993133e6628 keywords "quantum" assertion.
• 70967290a59911eca9a84993133e6628 temporalCoverage "2021-01-01/2021-01-01" assertion.
• 70967290a59911eca9a84993133e6628 yearCreated "2022" assertion.
• 70967290a59911eca9a84993133e6628 yearPublished "2022" assertion.
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