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Towards Quantum Scalable Data for Heterogeneous Computing Environments

Data scalability is a crucial characteristic in various classical applications. In this study, we introduce the concept of quantum scalable data. A hybrid quantum-classical architecture is proposed to support quantum scalable data, which includes scalable data definition, associated metadata, and operations in both classical and quantum parts. As a proof of concept, experiments of quantum machine learning are carried out with quantum scalable data. It is found that when more data layers are retained, classification performance will be increased. Also, scalability implementation of quantum data is much more straightforward than that of classical data. This could be considered an advantage of quantum data compared to classical data. To the best of our knowledge, this is the first work that introduces and experimentally proves the concept of quantum scalable data.