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Gabriele Lovicu

Publications and source records attributed to Gabriele Lovicu.

2 recordsLinked to original sources

A source of entangled photons based on a cavity-enhanced and strain-tuned GaAs quantum dot

A quantum-light source that delivers photons with a high brightness and a high degree of entanglement is fundamental for the development of efficient entanglement-based quantum-key distribution systems. Among all possible candidates, epitaxial quantum dots are currently emerging as one of the brightest sources of highly entangled photons. However, the optimization of both brightness and entanglement currently requires different technologies that are difficult to combine in a scalable manner. In this work, we overcome this challenge by developing a novel device consisting of a quantum dot embedded in a circular Bragg resonator, in turn, integrated onto a micromachined piezoelectric actuator. The resonator engineers the light-matter interaction to empower extraction efficiencies up to 0.69(4). Simultaneously, the actuator manipulates strain fields that tune the quantum dot for the generation of entangled photons with corrected fidelities to a maximally entangled state up to 0.96(1). This hybrid technology has the potential to overcome the limitations of the key rates that plague QD-based entangled sources for entanglement-based quantum key distribution and entanglement-based quantum networks.

quant-ph

FDEMtools: a MATLAB package for FDEM data inversion

Electromagnetic induction surveys are among the most popular techniques for non-destructive investigation of soil properties in order to detect the presence of either ground inhomogeneities or of particular substances. This work introduces a MATLAB package for the inversion of electromagnetic data collected by a ground conductivity meter. Based on a nonlinear forward model used to describe the interaction between an electromagnetic field and the soil, the software reconstructs either the electrical conductivity or the magnetic permeability of the soil with respect to depth, by a regularized damped Gauss-Newton method. The regularization part of the algorithm is based on a low-rank approximation of the Jacobian of the nonlinear model. Both the relaxation parameter and the regularization parameter are chosen by automatic procedures. The package allows the user to experiment with synthetic data sets and different regularization strategies, in order to compare them and draw conclusions.

math.NA