QDataset: Quantum Datasets for Machine Learning
Elija Perrier, Akram Youssry, Chris Ferrie

TL;DR
The paper introduces QDataSet, a comprehensive collection of 52 quantum datasets designed to support the development and benchmarking of quantum machine learning algorithms, filling a critical resource gap in the field.
Contribution
It provides the first large-scale, publicly available quantum datasets specifically created for training and testing quantum machine learning algorithms.
Findings
QDataSet enables benchmarking of quantum ML algorithms.
Datasets include simulations of one- and two-qubit systems with noise.
Workbooks demonstrate practical applications in quantum control and tomography.
Abstract
The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline and its maturity as a research discipline. Despite considerable advancements in recent years, the field of quantum machine learning (QML) has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of QML algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
