Quantum Datasets

Quantum datasets are collections of quantum states or quantum circuits used to train quantum machine learning algorithms or benchmark quantum hardware. Current research focuses on generating realistic and efficient quantum datasets, exploring methods like transformer-based models for circuit synthesis and leveraging quantum generative adversarial networks (QGANs) for data generation. These efforts aim to overcome challenges like the "curse of random quantum data" and enable the development of more effective quantum algorithms and improved quantum hardware characterization, ultimately advancing the field of quantum computing.