QDataSet, quantum datasets for machine learning.

Area of Science:

• Quantum Computing
• Machine Learning
• Data Science

Background:

• Machine learning (ML) has rapidly advanced due to large-scale datasets.
• Quantum machine learning (QML) currently lacks standardized, large-scale datasets for algorithm benchmarking.
• This gap hinders the development and application of QML algorithms.

Purpose of the Study:

• Introduce the QDataSet, a comprehensive dataset for QML research.
• Facilitate the training and benchmarking of QML algorithms.
• Support advancements in both theoretical and applied quantum computing.

Main Methods:

• Compiled 52 high-quality, publicly available datasets from simulations.
• Included data from one- and two-qubit systems.
• Incorporated simulations with and without noise to mimic real-world conditions.

Main Results:

• The QDataSet provides a rich resource for QML algorithm development.
• Datasets are structured for diverse applications in quantum computation.
• Accompanying workbooks demonstrate practical use cases in optimization.

Conclusions:

• The QDataSet addresses a critical need in the quantum machine learning field.
• It enables robust benchmarking and accelerates the development of QML algorithms.
• Facilitates practical applications in quantum control, spectroscopy, and tomography.