  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, we implement these ideas 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.
We perform also a similar analysis on the Rigetti superconducting quantum
computer. Therefore, we find 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.
