1,040 research outputs found
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 ,
a mutual indistinguishably of , and intrinsic
heralding efficiency. We measure on-chip quantum interference with a visibility
of 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
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