Signal-to-noise properties of correlation plenoptic imaging with chaotic light

Correlation Plenoptic Imaging (CPI) is a novel imaging technique, that exploits the correlations between the intensity fluctuations of light to perform the typical tasks of plenoptic imaging (namely, refocusing out-of-focus parts of the scene, extending the depth of field, and performing 3D reconstruction), without entailing a loss of spatial resolution. Here, we consider two different CPI schemes based on chaotic light, both employing ghost imaging: the first one to image the object, the second one to image the focusing element. We characterize their noise properties in terms of the signal-to-noise ratio (SNR) and compare their performances. We find that the SNR can be significantly higher and easier to control in the second CPI scheme, involving standard imaging of the object; under adequate conditions, this scheme enables reducing by one order of magnitude the number of frames for achieving the same SNR.Comment: 12 pages, 3 figure

A stroll in the energy landscape

We review recent results on the potential energy landscape (PES) of model liquids. The role of saddle-points in the PES in connecting dynamics to statics is investigated, confirming that a change between minima-dominated and saddle-dominated regions of the PES explored in equilibrium happens around the Mode Coupling Temperature. The structure of the low-energy saddles in the basins is found to be simple and hierarchically organized; the presence of saddles nearby in energy to the local minima indicates that, at non-cryogenic temperatures, entropic bottlenecks limit the dynamics.Comment: 8th International Workshop on Disordered Systems, Andalo (Trento), Italy, 12-15 March 200

Dinamiche di scambio nel Mediterraneo antico: il caso di Cerveteri

The ISCIMA-CNR has participated in the FIRB 2001 Project with research on “Trade dynamics in the ancient Mediterranean: the role of Etruria”. This title raises a number of diverse issues: continuity and discontinuity in trading circuits in the Mediterranean Basin; structure of production and movement of goods; study of the relationship between urban and rural areas involved in their production and consumption; role of the Etruscan ports in the dynamic of trade. In order to conduct a diachronic analysis on this subject, the Etruscan metropolis of Cerveteri has been proposed as a sample area. Since the 1980s, in fact, Cerveteri has been investigated by the CNR Institute through systematic surveys and excavations, resulting in a better understanding of the urban area and the surrounding territory. In particular, within the FIRB Project, the results of the research activity come from the analysis of settlement models, the production of ceramic typological lists, the application of innovative ICT methods to field archaeology, together with archaeoastronomical and spatial analysis techniques, the use of archaeometric research tools to analyse ceramic and metallic objects. The article also describes in detail an integrated approach to define the typology and study the spatial distribution of specific classes of ceramics (in particular the archaic pottery), which have been found during excavations in the central part of the urban plateau, in an area occupied by an open-air elliptical building

Deterministic entangling gates with nonlinear quantum photonic interferometers

The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements outcome and thus non-deterministic. Devising a fully deterministic, universal, and practically achievable quantum computing platform based on integrated photonic circuits is still an open challenge. Here we propose to exploit weakly nonlinear photonic devices to implement deterministic entangling quantum gates, following the definition of dual rail photonic qubits. It is shown that a universal set of single- and two-qubit gates can be designed by a suitable concatenation of few optical interferometric elements, with optimal fidelities arbitrarily close to 100% theoretically demonstrated through a bound constrained optimization algorithm. The actual realization would require the concatenation of a few tens of elementary operations, as well as on-chip optical nonlinearities that are compatible with some of the existing quantum photonic platforms, as it is finally discussed

A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks

An optimization problem in thermal insulation with Robin boundary conditions

We study thermal insulating of a bounded body $\Omega\subset \mathbb{R}^n$. Under a prescribed heat source $f\geq 0$, we consider a model of heat transfer between $\Omega$ and the environment determined by convection; this corresponds, before insulation, to Robin boundary conditions. The body is then surrounded by a layer of insulating material of thickness of size $\varepsilon>0$, and whose conductivity is also proportional to $\varepsilon$. This corresponds to the case of a small amount of insulating material, with excellent insulating properties. We then compute the $\Gamma$-limit of the energy functional $F_\varepsilon$ and prove that this is a functional $F$ whose minimizers still satisfy an elliptic PDEs system with a non uniform Robin boundary condition depending on the distribution of insulating layer around $\Omega$. In a second step we study the maximization of heat content (which measures the goodness of the insulation) among all the possible distributions of insulating material with fixed mass, and prove an optimal upper bound in terms of geometric properties. Eventually we prove a conjecture which states that the ball surrounded by a uniform distribution of insulating material maximizes the heat content