The emerging field of quantum data science aims to leverage properties of quantum mechanics to process data more efficiently and effectively than is possible with classical approaches alone. As a subset of quantum machine learning techniques, it involves topics including efficient storage, retrieval, and manipulation of datasets using quantum representations, such as gate-based circuits. Representative QIRG publications addressing quantum data representation are below, including automated production of quantum-read only memories and higher-dimensional kernel representation. We gratefully acknowledge Anametric, Inc. for contributing financial support to these projects.
Representative Publications
A. Sinha, E.R. Henderson, J.M. Henderson and M.A. Thornton, " Automated Quantum Memory Compilation with Improved Dynamic Range,"International Workshop on Quantum Computing Software (QCS22) /International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) , 14 pp., November 13, 2022.A. Sinha and M.A. Thornton, " Quantum Multiple Valued Kernel Circuits,"IEEE International Symposium on Multiple-Valued Logic (ISMVL) , May 18-20, 2022, pp. 1-8.