Neural networks for detecting multimode Wigner-negativity

Understand

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols.

• In the continuous variables encoding, quantum homodyne tomography requires an amount of measurements that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes.
• Here we introduce a new technique, based on a machine learning protocol with artificial Neural Networks, that allows to directly detect negativity of the Wigner function for multimode quantum states.
• We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically.