Experimental semi-device-independent tests of quantum channels published in Quantum Science and Technology

Quantum tomography is currently the mainly employed method to characterize a quantum system and therefore plays a fundamental role when trying to characterize the action of a particular channel. Nonetheless, quantum tomography works on the premise of a full characterization and description of the devices preparing the quantum state and realizing the measurements. Such an … Continua a leggere

Photonic implementation of boson sampling: a review published in Advanced Photonics!

Boson sampling is a computational problem that has recently been proposed as a candidate to obtain an unequivocal quantum computational advantage. The problem consists in sampling from the output distribution of indistinguishable bosons in a linear interferometer. There is strong evidence that such an experiment is hard to classically simulate, but it is naturally solved … Continua a leggere

OSA QIMV: Quantum Technologies in Rome from 4 to 6 April 2019

Quantum Information and Measurement (QIM) – V: Quantum Technologies Rome, 4-6 April 2019   Details at www.quantumlab.it/qim2019/ All accepted papers will be published and indexed as Conference Proceedings in OSA Technical Digest. Program Chairs Fabio Sciarrino, University of Rome La Sapienza David Lucas, University of Oxford Chairs Ian Walmsley (OSA President), UCL Irfan Siddiqi, University of … Continua a leggere

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

Tunable Two-Photon Quantum Interference of Structured Light published in Physical Review Letters!

The paper has been highlighted in Nature Photonics! Structured photons are nowadays an important resource in classical and quantum optics due to the richness of properties they show under propagation, focusing, and in their interaction with matter. Vectorial modes of light in particular, a class of modes where the polarization varies across the beam profile, … Continua a leggere

Pattern Recognition Techniques for Boson Sampling Validation published in Physical Review X!

The difficulty of validating large-scale quantum devices, such as boson samplers, poses a major challenge for any research program that aims to show quantum advantages over classical hardware. Towards this aim, we propose a novel data-driven approach, wherein models are trained to identify common pathologies using unsupervised machine-learning methods. We illustrate this idea by training … Continua a leggere