QDataset: Quantum Datasets for Machine Learning

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 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.