Tootfinder

DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control
Marco Borghesi, Lorenzo Sforni, Giuseppe Notarstefano
https://arxiv.org/abs/2506.15465

DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control
This article addresses the problem of data-driven numerical optimal control for unknown nonlinear systems. In our scenario, we suppose to have the possibility of performing multiple experiments (or simulations) on the system. Experiments are performed by relying on a data-driven tracking controller able to steer the system towards a desired reference. Our proposed DATA-DRIVEN PRONTO algorithm iteratively refines a tentative solution of the optimal control problem by computing an approximate des…

Versatile Wavelength-Division Multiplexed Quantum Key Distribution Network Operating Simultaneously in the O and C Bands
Davide Scalcon, Matteo Padovan, Paolo Villoresi, Giuseppe Vallone, Marco Avesani
https://arxiv.org/abs/2507.11175

Versatile Wavelength-Division Multiplexed Quantum Key Distribution Network Operating Simultaneously in the O and C Bands
Ongoing technological progress is accelerating the commercial and global-scale deployment of Quantum Key Distribution (QKD). Its ability to enable unconditionally secure communication is expected to be a key feature of future telecommunication networks, and practical demonstrations of QKD network implementations in real-world environments are crucial for ensuring reliable adoption. In this work, we demonstrate a four-node photonic QKD network that employs versatile and cost-effective wavelength…

Multishot Capacity of Networks with Restricted Adversaries
Giuseppe Cotardo, Gretchen L. Matthews, Alberto Ravagnani, Julia Shapiro
https://arxiv.org/abs/2506.03361

Multishot Capacity of Networks with Restricted Adversaries
We investigate adversarial network coding and decoding, focusing on the multishot regime and when the adversary is restricted to operate on a vulnerable region of the network. Errors can occur on a proper subset of the network edges and are modeled via an adversarial channel. The paper contains both bounds and capacity-achieving schemes for the Diamond Network, the Mirrored Diamond Network, and generalizations of these networks. We also initiate the study of the capacity of 3-level networks in …

Local Clustering in Hypergraphs through Higher-Order Motifs
Giuseppe F. Italiano, Athanasios L. Konstantinidis, Anna Mpanti, Fariba Ranjbar
https://arxiv.org/abs/2507.10570

Local Clustering in Hypergraphs through Higher-Order Motifs
Hypergraphs provide a powerful framework for modeling complex systems and networks with higher-order interactions beyond simple pairwise relationships. However, graph-based clustering approaches, which focus primarily on pairwise relations, fail to represent higher-order interactions, often resulting in low-quality clustering outcomes. In this work, we introduce a novel approach for local clustering in hypergraphs based on higher-order motifs, small connected subgraphs in which nodes may be lin…

Selective decoupling in multi-level quantum systems by the SU(2) sign anomaly
Giorgio Anfuso, Giulia Piccitto, Vittorio Romano, Elisabetta Paladino, Giuseppe Falci
https://arxiv.org/abs/2507.12056

Selective decoupling in multi-level quantum systems by the SU(2) sign anomaly
We investigate dynamical decoupling operated by $2π$-pulses in a two-level subspaces of a multilevel system showing that it may leads to selective decoupling. This provides a flexible strategy for decoupling transitions in a quantum network, when control to directly address them is not available which can be use to control internode interaction or actively suppress decoherence

Beer Path Problems in Temporal Graphs
Andrea D'Ascenzo, Giuseppe F. Italiano, Sotiris Kanellopoulos, Anna Mpanti, Aris Pagourtzis, Christos Pergaminelis
https://arxiv.org/abs/2507.08685

Beer Path Problems in Temporal Graphs
Computing paths in graph structures is a fundamental operation in a wide range of applications, from transportation networks to data analysis. The beer path problem, which captures the option of visiting points of interest, such as gas stations or convenience stops, prior to reaching the final destination, has been recently introduced and extensively studied in static graphs. However, existing approaches do not account for temporal information, which is often crucial in real-world scenarios. Fo…

Quantum Spin Glass in the Two-Dimensional Disordered Heisenberg Model via Foundation Neural-Network Quantum States
Luciano Loris Viteritti, Riccardo Rende, Giacomo Bracci Testasecca, Jacopo Niedda, Roderich Moessner, Giuseppe Carleo, Antonello Scardicchio
https://arxiv.org/abs/2507.05073…

Quantum Spin Glass in the Two-Dimensional Disordered Heisenberg Model via Foundation Neural-Network Quantum States
We investigate the two-dimensional frustrated quantum Heisenberg model with bond disorder on nearest-neighbor couplings using the recently introduced Foundation Neural-Network Quantum States framework, which enables accurate and efficient computation of disorder-averaged observables with a single variational optimization. Simulations on large lattices reveal an extended region of the phase diagram where long-range magnetic order vanishes in the thermodynamic limit, while the overlap order param…

Early linguistic fingerprints of online users who engage with conspiracy communities
Francesco Corso, Giuseppe Russo, Francesco Pierri, Gianmarco De Francisci Morales
https://arxiv.org/abs/2506.05086

Early linguistic fingerprints of online users who engage with conspiracy communities
Online social media platforms are often seen as catalysts for radicalization, as they provide spaces where extreme beliefs can take root and spread, sometimes leading to real-world consequences. Conspiracy theories represent a specific form of radicalization that is notoriously resistant to online moderation strategies. One explanation for this resilience is the presence of a "conspiratorial mindset", a cognitive framework that fundamentally shapes how conspiracy believers perceive reality. How…

Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
Afila Ajithkumar Sophiya, Sepehr Maleki, Giuseppe Bruni, Senthil K. Krishnababu
https://arxiv.org/abs/2506.19503

Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
Physics-Informed Neural Networks (PINNs) have emerged as a promising computational framework for solving differential equations by integrating deep learning with physical constraints. However, their application in gas turbines is still in its early stages, requiring further refinement and standardization for wider adoption. This survey provides a comprehensive review of PINNs in Industrial Gas Turbines (IGTs) research, highlighting their contributions to the analysis of aerodynamic and aeromech…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Percolation thresholds and connectivity in quantum networks
Andrea De Girolamo, Giuseppe Magnifico, Cosmo Lupo