Near-ideal spontaneous photon sources in silicon quantum photonics

While integrated photonics is a robust platform for quantum information processing, architectures for photonic quantum computing place stringent demands on high quality information carriers. Sources of single photons that are highly indistinguishable and pure, that are either near-deterministic or heralded with high efficiency, and that are suitable for mass-manufacture, have been elusive. Here, we demonstrate on-chip photon sources that simultaneously meet each of these requirements. Our photon sources are fabricated in silicon using mature processes, and exploit a novel dual-mode pump-delayed excitation scheme to engineer the emission of spectrally pure photon pairs through intermodal spontaneous four-wave mixing in low-loss spiralled multi-mode waveguides. We simultaneously measure a spectral purity of $0.9904 \pm 0.0006$, a mutual indistinguishably of $0.987 \pm 0.002$, and $>90\%$ intrinsic heralding efficiency. We measure on-chip quantum interference with a visibility of $0.96 \pm 0.02$ between heralded photons from different sources. These results represent a decisive step for scaling quantum information processing in integrated photonics

An adaptive consensus based method for multi-objective optimization with uniform Pareto front approximation

In this work we are interested in stochastic particle methods for multi-objective optimization. The problem is formulated using parametrized, single-objective sub-problems which are solved simultaneously. To this end a consensus based multi-objective optimization method on the search space combined with an additional heuristic strategy to adapt parameters during the computations is proposed. The adaptive strategy aims to distribute the particles uniformly over the image space by using energy-based measures to quantify the diversity of the system. The resulting metaheuristic algorithm is mathematically analyzed using a mean-field approximation and convergence guarantees towards optimal points is rigorously proven. In addition, a gradient flow structure in the parameter space for the adaptive method is revealed and analyzed. Several numerical experiments shows the validity of the proposed stochastic particle dynamics and illustrate the theoretical findings

Consensus based optimization with memory effects: random selection and applications

In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a consensus dynamics inspired by social interactions among individuals. The consensus point is computed taking into account the past positions of all particles. While sharing features with the popular Particle Swarm Optimization (PSO) method, the exploratory behavior is fundamentally different and allows better control over the convergence of the particle system. We discuss some implementation aspects which lead to an increased efficiency while preserving the success rate in the optimization process. In particular, we show how employing a random selection strategy to discard particles during the computation improves the overall performance. Several benchmark problems and applications to image segmentation and Neural Networks training are used to validate and test the proposed method. A theoretical analysis allows to recover convergence guarantees under mild assumptions on the objective function. This is done by first approximating the particles evolution with a continuous-in-time dynamics, and then by taking the mean-field limit of such dynamics. Convergence to a global minimizer is finally proved at the mean-field level

Enabling On-Device Continual Learning with Binary Neural Networks and Latent Replay

On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities. This challenge is primarily rooted in two key issues: first, the memory available on embedded devices is typically insufficient to accommodate the memory-intensive back-propagation algorithm, which often relies on floating-point precision. Second, the development of learning algorithms on models with extreme quantization levels, such as Binary Neural Networks (BNNs), is critical due to the drastic reduction in bit representation. In this study, we propose a solution that combines recent advancements in the field of Continual Learning (CL) and Binary Neural Networks to enable on-device training while maintaining competitive performance. Specifically, our approach leverages binary latent replay (LR) activations and a novel quantization scheme that significantly reduces the number of bits required for gradient computation. The experimental validation demonstrates a significant accuracy improvement in combination with a noticeable reduction in memory requirement, confirming the suitability of our approach in expanding the practical applications of deep learning in real-world scenarios

Detecting Morphing Attacks via Continual Incremental Training

Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging. We hypothesize that the recent Continual Learning (CL) paradigm may represent an effective solution to enable incremental training, even through multiple sites. Indeed, a basic assumption of CL is that once a model has been trained, old data can no longer be used in successive training iterations and in principle can be deleted. Therefore, in this paper, we investigate the performance of different Continual Learning methods in this scenario, simulating a learning model that is updated every time a new chunk of data, even of variable size, is available. Experimental results reveal that a particular CL method, namely Learning without Forgetting (LwF), is one of the best-performing algorithms. Then, we investigate its usage and parametrization in Morphing Attack Detection and Object Classification tasks, specifically with respect to the amount of new training data that became available.Comment: Paper accepted in IJCB 2023 conferenc

PRECISE Photonic hybRid EleCtromagnetic SolvEr

The Photonic hybRid EleCtromagnetic SolvEr (PRECISE) is a Matlab based library to model large and complex photonics integrated circuits. Each circuit is modularly described in terms of waveguide segments connected through multiport nodes. Linear, nonlinear, and dynamical phenomena are simulated by solving the system of differential equations describing the effect to be considered. By exploiting the steady state approximation of the electromagnetic field within each node device, the library can handle large and complex circuits even on desktop PC. We show that the steady state assumption is fulfilled in a broad number of applications and we compare its accuracy with analytical model (coupled mode theory) and experimental results. PRECISE is highly modular and easily extensible to handle equations different from those already implemented and is, thus, a flexible tool to model the increasingly complex photonic circuits.Comment: 21 pages, 13 figure

Chaotic dynamics in coupled resonator sequences

Optically induced thermal and free carrier nonlinearities in silicon micro-ring resonator influence their behavior. They can be either deleterious by making them instable and by driving their resonances out of the designed wavelengths, or enabler of different applications. Among the most interesting one, there are optical bistability and self induced oscillations. These lead to all optical logic, signal modulation, optical memories and applications in neural networks. Here, we theoretically and experimentally demonstrate that when many resonators are coupled together, thermal and free carrier nonlinearities induce also chaos. The chaotic dynamics are deeply analyzed using experimentally reconstructed phase space trajectories and the tool of Lyapunov exponents

Micro‐PIXE determination of Zr in rutile: an application to geothermometry of high‐P rocks from the western Alps (Italy)

AbstractThe Western Alps of Northern Italy mostly consist of lithotectonic units which re‐crystallised and were metamorphosed at high depth in a subduction zone. During their exhumation to shallow crustal levels, however, the high‐pressure (high‐P) mineral assemblages were pervasively re‐equilibrated under low‐pressure (low‐P) conditions, making difficult to estimate the metamorphic thermal peak.Rutile [TiO2] is a typical high‐P mineral, occurring as relict phase in low‐P re‐equilibrated metamorphic rocks. Recent studies suggest that, in thermodynamic systems buffered by the occurrence of quartz and zircon in the rock, Zr content in rutile is a temperature–dependent function that can be modelled quantitatively.An application of rutile Zr‐geothermometer to continental and oceanic rocks of the Western Alps, pervasively re‐equilibrated under low‐P conditions, is presented.The selected rutile crystals were analysed by PIXE using a microbeam set‐up at the LABEC laboratory of INFN in Florence. The PIXE spectra and maps were processed by Geopixe software package. Micro‐PIXE analyses allowed determining the concentration and the distribution of Zr.Results obtained by applying the rutile Zr‐geothermometer gave a more precise indication about the temperatures of the metamorphic conditions suffered by Alpine metamorphic rocks with respect to phase relations and conventional geothermometry, showing that determination of Zr concentration by micro‐PIXE technique is a useful tool to reconstruct metamorphic events.The continental units, outcropping in separate zones of Western Alps, show two slightly different thermal peaks (Tmean = 530 ± 10 °C and Tmean = 555 ± 10 °C) for the same metamorphic event. The oceanic units provide Tmean estimates of 575 ± 10 °C slightly higher than the continental units. Copyright © 2008 John Wiley & Sons, Ltd

Four Wave Mixing control in a photonic molecule made by silicon microring resonators

Abstract Four Wave Mixing (FWM) is the main nonlinear interaction in integrated silicon devices, which finds diffuse use in all-optical signal processing and wavelength conversion. Despite the numerous works on coupled resonator devices, which showed record conversion efficiencies and broadband operation, the possibility to coherently control the strength of the stimulated FWM interaction on a chip has received very limited attention. Here, we demonstrate both theoretically and experimentally, the manipulation of FWM in a photonic molecule based on two side coupled silicon microring resonators. The active tuning of the inter-resonator phase and of their eigenfrequencies allows setting the molecule in a sub-radiant state, where FWM is enhanced with respect to the isolated resonators. On the other hand, we can reconfigure the state of the photonic molecule to have energy equipartition among the resonators, and suppress FWM by making the two Signal waves to interfere destructively in the side coupled waveguides. This work constitutes an experimental demonstration of the control of a nonlinear parametric interaction via coherent oscillation phenomena in an integrated optical device