Experimental Quantum Embedding for Machine Learning

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