Experimental Property Reconstruction in a Photonic Quantum Extreme Learning Machine.

Recent developments have led to the possibility of embedding machine learning tools into experimental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum and performing projective measurements over a fixed basis.

Neural Networks for Detecting Multimode Wigner Negativity.

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement 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 us to directly detect negativity of the Wigner function for multimode quantum states.

Adaptive tracking of enzymatic reactions with quantum light.

Enzymes are essential to maintain organisms alive. Some of the reactions they catalyze are associated with a change in reagents chirality, hence their activity can be tracked by using optical means. However, illumination affects enzyme activity: the challenge is to operate at low-intensity regime avoiding loss in sensitivity.

Calibration of Quantum Sensors by Neural Networks.

Introducing quantum sensors as a solution to real world problems demands reliability and controllability outside of laboratory conditions. Producers and operators ought to be assumed to have limited resources readily available for calibration, and yet, they should be able to trust the devices. Neural networks are almost ubiquitous for similar tasks for classical sensors: here we show the applications of this technique to calibrating a quantum photonic sensor.

Stimulated emission tomography: beyond polarization.

In this work, we demonstrate the use of stimulated emission tomography to characterize a hyperentangled state generated by spontaneous parametric downconversion in a cw-pumped source. In particular, we consider the generation of hyperentangled states consisting of photon pairs entangled in polarization and path. These results extend the capability of stimulated emission tomography beyond the polarization degree of freedom and demonstrate the use of this technique to study states in higher dimension Hilbert spaces.

Assessing frequency correlation through a distinguishability measurement.

The simplicity of a question, such as wondering whether or not correlations characterize a certain system, collides with the experimental difficulty of accessing such information. Here we present a low-demanding experimental approach that refers to the use of a metrology scheme to obtain a conservative estimate of the strength of frequency correlations. Our test bed is the widespread case of a photon pair produced per downconversion.

Experimental nonlocality-based network diagnostics of multipartite entangled states.

We introduce a novel diagnostic scheme for multipartite networks of entangled particles, aimed at assessing the quality of the gates used for the engineering of their state. Using the information gathered from a set of suitably chosen multiparticle Bell tests, we identify conditions bounding the quality of the entangled bonds among the elements of a register. We illustrate the effectiveness of our proposal by characterizing a quantum resource engineered combining two-photon hyperentanglement and photonic-chip technology.