7 citations · 7 across the 4 of their papers we have counts for
5 papers
A Tensor Network Framework for Interpretable Graph Analysis of Brain Networks
Domenico Pomarico, Giuseppe Magnifico, Alessandro Grecucci +10
Identifying robust neurobiological signatures of brain disorders requires machine learning approaches that combine predictive performance with interpretable representations of feat…
Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations
Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez +12
A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embeddings and Matrix Product Stat…
Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Domenico Pomarico, Roberto Cilli, Alfonso Monaco +10
Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting struct…
Grokking as an entanglement transition in tensor network machine learning
Domenico Pomarico, Alfonso Monaco, Giuseppe Magnifico +11
Grokking is a intriguing phenomenon in machine learning where a neural network, after many training iterations with negligible improvement in generalization, suddenly achieves high…
Directional emission and photon bunching from a qubit pair in waveguide
M. Maffei, D. Pomarico, P. Facchi +3
Waveguide quantum electrodynamics represents a powerful platform to generate entanglement and tailor photonic states. We consider a pair of identical qubits coupled to a parity inv…