Deep learning of quantum entanglement from incomplete measurements.

The quantification of the entanglement present in a physical system is of paramount importance for fundamental research and many cutting-edge applications. Now, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that, by using neural networks, we can quantify the degree of entanglement without the need to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result using networks trained using exclusively simulated data. Last, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.

Exploring the ultimate limits: super-resolution enhanced by partial coherence.

The resolution of separation of two elementary signals forming a partially coherent superposition, defined by quantum Fisher information and normalized with respect to detection probabilities, is always limited by the resolution of incoherent mixtures. However, when the partially coherent superpositions are prepared in a controlled way, the precision can be enhanced by up to several orders of magnitude above this limit. Coherence also allows the sorting of information about various parameters into distinct channels as demonstrated by the parameter of separation linked with the anti-phase superposition and the centroid position linked with the in-phase superposition.

Compressed sensing of twisted photons.

The ability to completely characterize the state of a system is an essential element for the emerging quantum technologies. Here, we present a compressed-sensing-inspired method to ascertain any rank-deficient qudit state, which we experimentally encode in photonic orbital angular momentum. We efficiently reconstruct these qudit states from a few scans with an intensified CCD camera. Since it only requires a small number of intensity measurements, our technique provides an easy and accurate way to identify quantum sources, channels, and systems.