Experimental Quantum Embedding for Machine Learning. Issue 8 (9th June 2022)

• Record Type: Journal Article
• Authors: Gianani, Ilaria; Mastroserio, Ivana; Buffoni, Lorenzo; Bruno, Natalia; Donati, Ludovica; Cimini, Valeria; Barbieri, Marco; Cataliotti, Francesco S.; Caruso, Filippo
• Is Part Of: Advanced quantum technologies. Volume 5:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-09
• Subjects: experimental quantum technologies -- noisy intermediate size quantum devices -- quantum machine learning -- quantum optics -- ultra‐cold atoms; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202100140
• Languages: English
• ISSNs: 2511-9044

Abstract

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. Abstract : The concept of quantum embedding is experimentally implemented, that is, mapping classical data to be classified into a large quantum Hilbert space, by engineering two different platforms, based on quantum optics and ultra‐cold atoms, and also testing on the Rigetti superconducting quantum computer, hence paving the way for future investigations on quantum machine learning via hybrid quantum technologies.