Photonic REservoir computing QUantum correlation Set ORacle

In the last decades, a new type of technology has been developed, which is based on quantum mechanics: the theory describing nature in its microscopic scale. Indeed, its counterintuitive laws have proven able to tackle problems that were considered out of reach and achieve remarkable advantages. For instance, quantum hardware can tackle in a few seconds computational problems that are solvable in thousands of years by the most powerful classical computers. Furthermore, quantum systems offer the tools to obtain the highest level of security in communications, robust against any kind of eavesdropper. Despite these premises, however, this field, especially from the experimental point of view, is still very young and many challenges are still open. For example, it is still an open question how to verify the correct functioning of such technologies and assess that they are based on quantum features and not just simulating them.
PREQUrSOR goes in that direction and offers an experimental tool for detecting quantum phenomena in arbitrary processes. This will be done by combining the study of quantum mechanical foundations with machine learning, which is a branch of artificial intelligence aiming to make computers “learn” from given examples and then correctly deal with previously unseen data. More in detail, we will build a prototype of an artificial neural network, a learning model resembling the structure of a human brain, on quantum photonic hardware. This model will use the quantum behavior of photons, to classify trustworthy quantum devices from faulty ones.
The main novelties of this project are two: on one hand, we will provide the first implementation of an artificial neural network on a photonic platform. Given the wide use of classical artificial neural networks, providing their quantum version will introduce a very versatile tool for tackling new and diverse challenges. However, this is challenging because the learning process of a neural network requires nonlinear effects, which can be obtained through the interaction of quantum systems with the environment. However, this can also lead to loosing part of their quantum characteristics. A solution to this apparent deadlock is given by a novel photonic device, the quantum memristor, developed by the University of Vienna, which displays a behavior that is similar to that of brain synapses, while preserving the quantum features of photons. The second interesting feature of this project is that we will not to tackle a classical problem and look for a quantum speed-up or enhanced accuracy in the solution. Indeed, the proposed verification is only feasible through a quantum apparatus, giving an immediate practical use of these results. On the contrary, looking for an advantage would have been a much more elusive scope, given the often unfair comparison between the early stage of quantum technologies and the maturity of the classical counterpart.