Experimental learning of quantum states

Published on March 29 in Science Advances, experimentally demonstrates for the first time that it is possible to efficiently describe a quantum state, using the tools of computational learning theory. In fact it is known that in general the description of a quantum state requires a number of parameters that grows exponentially with the number … Continua a leggere

Perspectives on experimental quantum causality

Inferring causal relations from empirical data is a central task in any scientific inquiry. To that aim, a mathematical theory of causality has been developed, not only stating under which conditions such inference becomes possible but also offering a formal framework to reason about cause and effect within quantum theory. This perspective article provides an … Continua a leggere

Visual assessment of multi-photon interference published in Quantum Science and Technology!

Classical machine learning algorithms can provide insights on high-dimensional processes that are hardly accessible with conventional approaches. As a notable example, t-distributed Stochastic Neighbor Embedding (t-SNE) represents the state of the art for visualization of data sets of large dimensionality. An interesting question is then if this algorithm can provide useful information also in quantum … Continua a leggere

Experimental multiphase estimation on a chip published in Optica!

Multiparameter estimation is a general problem that aims at measuring unknown physical quantities, obtaining high precision in the process. In this context, the adoption of quantum resources promises a substantial boost in achievable performances with respect to the classical case. However, several open problems remain to be addressed in the multiparameter scenario. A crucial requirement … Continua a leggere