Tootfinder

Emergence of Generic Entanglement Structure in Doped Matchgate Circuits
Alessio Paviglianiti, Luca Lumia, Emanuele Tirrito, Alessandro Silva, Mario Collura, Xhek Turkeshi, Guglielmo Lami
https://arxiv.org/abs/2507.12526

Emergence of Generic Entanglement Structure in Doped Matchgate Circuits
Free fermionic Gaussian, a.k.a. matchgate, random circuits exhibit atypical behavior compared to generic interacting systems. They produce anomalously slow entanglement growth, characterized by diffusive scaling $S(t) \sim \sqrt{t}$, and evolve into volume-law entangled states at late times, $S \sim N$, which are highly unstable to measurements. Here, we investigate how doping such circuits with non-Gaussian resources (gates) restores entanglement structures of typical dynamics. We demonstrate …

Optical Cavity in Relativistic Regime for Laser Propulsion
Francesco Lorenzi, Luca Salasnich, Maria Guglielmina Pelizzo
https://arxiv.org/abs/2508.12442 https://

Optical Cavity in Relativistic Regime for Laser Propulsion
Laser propulsion has been proposed for relativistic interstellar flights, but it faces the significant challenge of requiring extremely powerful laser radiation due to the inherently low momentum transfer between the beam and the sail. The photon-recycling technique enhances thrust by transferring momentum through multiple reflections within a cavity setup, formed by the lightsail and a ground-based mirror in a laser system array. In this work, a delay differential model is developed to describ…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Sol...
Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino

Black Hole - Neutron Star and Binary Neutron Star Mergers from Population III and II stars
Benedetta Mestichelli, Michela Mapelli, Filippo Santoliquido, Manuel Arca Sedda, Marica Branchesi, Lavinia Paiella, Guglielmo Costa, Giuliano Iorio, Matthew Mould, Veronika Lipatova, Boyuan Liu, Ralf S. Klessen
https://arxiv.org/abs/2506.1…

Black Hole - Neutron Star and Binary Neutron Star Mergers from Population III and II stars
Population III (Pop.$~$III) stars are expected to be massive and to undergo minimal mass loss due to their lack of metals, making them ideal progenitors of black holes and neutron stars. Here, we investigate the formation and properties of binary neutron star (BNS) and black hole-neutron star (BHNS) mergers originating from Pop.$~$III stars, and compare them to their metal-enriched Population II (Pop.$~$II) counterparts, focusing on their merger rate densities (MRDs), primary masses and delay t…

Nonlinear Joint Spectral Radius
Piero Deidda, Nicola Guglielmi, Francesco Tudisco
https://arxiv.org/abs/2507.11314 https://arxiv.org/…

Nonlinear Joint Spectral Radius
We introduce a nonlinear extension of the joint spectral radius (JSR) for switched discrete-time dynamical systems governed by sub-homogeneous and order-preserving maps acting on cones. We show that this nonlinear JSR characterizes both the asymptotic stability of the system and the divergence or convergence rate of trajectories originating from different points within the cone. Our analysis establishes upper and lower bounds on the nonlinear JSR of a sub-homogeneous family via the JSRs of two …

Gap-SBM: A New Conceptualization of the Shifted Boundary Method with Optimal Convergence for the Neumann and Dirichlet Problems
J. Haydel Collins, Kangan Li, Alexei Lozinski, Guglielmo Scovazzi
https://arxiv.org/abs/2508.09613

Gap-SBM: A New Conceptualization of the Shifted Boundary Method with Optimal Convergence for the Neumann and Dirichlet Problems
We propose and mathematically analyze a new Shifted Boundary Method for the treatment of Dirichlet and Neumann boundary conditions, with provable optimal accuracy in the $L^2$- and $H^1$-norms of the error. The proposed method is built on three stages. First, the distance map between the SBM surrogate boundary and the true boundary is used to construct an approximation to the geometry of the gap between the two. Then, the representations of the numerical solution and test functions are extended…

Equivalence of stationary dynamical solutions in a directed chain and a Delay Differential Equation of neuroscientific relevance
Giulio Colombini, Nicola Guglielmi, Armando Bazzani
https://arxiv.org/abs/2506.11654

Equivalence of stationary dynamical solutions in a directed chain and a Delay Differential Equation of neuroscientific relevance
While synchronized states, and the dynamical pathways through which they emerge, are often regarded as the paradigm to understand the dynamics of information spreading on undirected networks of nonlinear dynamical systems, when we consider directed network architectures, dynamical stationary states can arise. To study this phenomenon we consider the simplest directed network, a single cycle, and excitable FitzHugh-Nagumo (FHN) neurons. We show numerically that a stationary dynamical state emerg…

Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV
Roberto Balestri, Guglielmo Pescatore
https://arxiv.org/abs/2508.07010 https://ar…

Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV
Serialized television narratives present significant analytical challenges due to their complex, temporally distributed storylines that necessitate sophisticated information management. This paper introduces a multi-agent system (MAS) designed to extract and analyze narrative arcs by implementing principles of computational memory architectures. The system conceptualizes narrative understanding through analogues of human memory: Large Language Models (LLMs) provide a form of semantic memory for…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Guglielmo Padula, Gianluigi Rozza
https://arxiv.org/abs/2506.09721 https…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Solving and optimising Partial Differential Equations (PDEs) in geometrically parameterised domains often requires iterative methods, leading to high computational and time complexities. One potential solution is to learn a direct mapping from the parameters to the PDE solution. Two prominent methods for this are Data-driven Non-Intrusive Reduced Order Models (DROMs) and Parametrised Physics Informed Neural Networks (PPINNs). However, their accuracy tends to degrade as the number of geometric p…

Sum-frequency-based photon-number-resolving detector for telecom wavelengths
Silvia Cassina, Alex Pozzoli, Guglielmo Vesco, Marco Lamperti, Marco Marangoni, Alessia Allevi
https://arxiv.org/abs/2508.02203

Sum-frequency-based photon-number-resolving detector for telecom wavelengths
The use of C-band wavelengths in the field of quantum communication has grown significantly, driving the need for versatile detection solutions, especially in the low intensity domain. Among the desirable features for such detectors, photon-number-resolving (PNR) capability is particularly valuable, since it can offer new possibilities for enhancing security of communication protocols. In this paper, we present the implementation of a receiver that combines low-cost PNR detectors with nonlinear…