  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 practitioners to
use the QDataSet to solve problems in applied quantum computation, such as
quantum control, quantum spectroscopy and tomography. Accompanying the datasets
on the associated GitHub repository are a set of workbooks demonstrating the
use of the QDataSet in a range of optimisation contexts.
