Experimental Quantum Embedding for Machine Learning

Year: 2022

Authors: Gianani I.; Mastroserio I.; Buffoni L.; Bruno N.; Donati L.; Cimini V.; Barbieri M.; Cataliotti F.S.; Caruso F.

Autors Affiliation: Univ Roma Tre, Dipartimento Sci, I-00146 Rome, Italy; Univ Firenze, LENS, I-50019 Sesto Fiorentino, Italy; Univ Firenze, Dipartimento Fis & Astron, I-50019 Sesto Fiorentino, Italy; Ist Nazl Ottica CNR INO, Largo Enrico Fermi 6, I-50125 Florence, Italy; Univ Napoli Federico II, Dipartimento Fis Ettore Pancini, Naples, Italy.

Abstract: The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, these ideas are implemented by engineering two different experimental platforms, based on quantum optics and ultra-cold atoms, respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. A similar analysis is also performed on the Rigetti superconducting quantum computer. Therefore, it is found that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.

Journal/Review: ADVANCED QUANTUM TECHNOLOGIES

Volume: 5 (8)      Pages from: 2100140-1  to: 2100140-8

More Information: F.C. was financially supported by the European Union´s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS). N.B. was financially supported by the Qombs Project, FET Flagship on Quantum Technologies grant no. 820419.
Open Access Funding provided by Universita degli Studi di Firenze within the CRUI-CARE Agreement.
KeyWords: experimental quantum technologies; noisy intermediate size quantum devices; quantum machine learning; quantum optics; ultra-cold atom
DOI: 10.1002/qute.202100140

Citations: 9
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