After 18 months since the beginning of project CAPABLE, all the partners (CNR-IFN, Sapienza university and ICFO) have met in Barcelona (Spain) for the presentation of the current results and to discuss the next experiments. A warm thanks to ICFO and in particular to Dr. Hugues de Riedmatten, that hosted the meeting and gave the opportunity to all the three partners to share new ideas for the development of the project in the next months.
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.
Classical machine learning algorithms can provide insights on high-dimensional processes that are hardly accessible with conventional approaches. In this work we apply t-distributed Stochastic Neighbor Embedding (t-SNE) to probe the spatial distribution of n-photon events in m-dimensional Hilbert spaces, showing that its findings can be beneficial for validating genuine quantum interference in boson sampling experiments. We envisage that this approach will inspire further theoretical investigations, for instance for a reliable assessment of quantum computational advantage.