Efforts to develop quantum computers are motivated by the promise of a tremendous speedup in several computational tasks such as quantum simulation or factoring. A milestone in this quest will be to provide evidence of quantum supremacy, which occurs when a quantum device solves a family of problems faster than state-of-the-art classical computers. The technological race toward this achievement goes hand in hand with the development of classical protocols that can discern genuine quantum processes. Here, we provide a step forward in this direction by presenting a machine-learning algorithm to detect malfunctions within a class of quantum hardware used to demonstrate quantum supremacy, relying only on experimental data.
3D nano-optical devices created directly inside dielectric crystals like YAG and sapphire by exploiting femtosecond laser pulses. This discovery is very important, because to date, through the conventional techniques of micro- and nano-processing, it is only possible to modify these crystals on their surface, thus obtaining purely 2D structures. These results pave the way for the development of new-generation photonic devices.
Experimental implementation of a reconfigurable integrated multimode interferometer designed for simultaneous estimation of two optical phases. We verify the high-fidelity operation of the implemented device and demonstrate quantum-enhanced performances in two-phase estimation with respect to the best classical case, post-selected to the number of detected coincidences. This device can be employed to test general adaptive multiphase protocols due to its high reconfigurability level, and represents a powerful platform to investigate the multiparameter estimation scenario.