An experimental quantum classifier

The experimental optimization is a crucial step to improve the performance of the quantum classifier. Theoretical results (red star) are not the optimal experimental configurations (blue area)
As in previous data re-uploading works, we can see how increasing the number of layers improves the final results. Top: 1, 2, 3 data re-uploadings. Bottom: comparison for 4 re-uploadings in the QPU and simulation.
More datasets can be classified successfully, for example:
A comparison between experimental results against classical algorithms is possible. As you can see, the experimental result (QPU column) is comparable to NNs and SVCs. This column will be filled in the future.