Ricerc@Sapienza

A fundamental milestone for quantum technology is represented by the capability of reaching the regime of quantum advantage, namely the scenario where a quantum device is able of solving a specific problem faster than any classical system. Boson Sampling, a classically-hard computational task, represents one of the most promising approach to reach such quantum advantage regime in a photonic platform. Besides the current advances in the technology, enabling the implementation of progressively larger photonic systems, a fundamental aspect can be found in the certification problem. More specifically, when reaching the quantum advantage regime where a classical system will not be able to solve the task, it is necessary to exploit suitable methodologies to certify the output of the corresponding quantum computation. In the Boson Sampling case, it is necessary to verify that the quantum device solving the task is sampling from the correct distribution, and is not affected by physically-motivated sources of noise. In this context, partial photon distinguishability is one of the most significant sources of imperfections, that can spoil the complexity of the classical computation if the noise level is above a given threshold.

In this project, we aim at developing and testing experimentally in an integrated platform validation tests capable of addressing certification against partial photon distinguishability. In particular, we will investigate the adoption of Machine learning protocols for such task, being the latter algorithms particularly suitable to deal with large data sets and to find hidden patterns. The obtained results are expected to increase the current state-of-art capability of verifying Boson Sampling computation, and are expected to be of significance for certification of other quantum computation and simulation platforms.