7 citations · 10 across the 7 of their papers we have counts for
10 papers
Statistical mechanics of multipartite entanglement in hypergraph states
Paolo Scarafile, Giorgia Trotta, Paolo Facchi +4
We investigate multipartite entanglement in a particular family of pure -qubit hypergraph states through a statistical-mechanics framework, where the average bipartite purity ma…
Generation of entanglement and magic via continuous homodyne monitoring of a qubit pair
Debmalya Das, Giuseppe Magnifico, Maria Maffei
Continuous weak measurements of quantum systems are of great relevance in quantum foundations and applications. They can be achieved by probing the quantum system of interest by re…
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…
Multipartite entanglement of random states of qubits
Giorgia Trotta, Paolo Scarafile, Paolo Facchi +5
We investigate multipartite entanglement via the statistical properties of pure quantum states of n-qubits. By analyzing the distribution of purity among balanced bipartitions, we…
Superradiant decay in non-Markovian Waveguide Quantum Electrodynamics
Rosa Lucia Capurso, Giuseppe Calajó, Simone Montangero +5
An array of initially excited emitters coupled to a one-dimensional waveguide exhibits superradiant decay under the Born-Markov approximation, manifested as a coherent burst of pho…
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…
Dynamical cluster-based strategy for improving tensor network algorithms in quantum circuit simulations
Andrea De Girolamo, Paolo Facchi, Peter Rabl +3
We optimize matrix-product state-based algorithms for simulating quantum circuits with finite fidelity, specifically the time-evolving block decimation (TEBD) and the density-matri…
Non-Markovian dynamics of generation of bound states in the continuum via single-photon scattering
Giuseppe Magnifico, Maria Maffei, Domenico Pomarico +4
The excitation of bound states in the continuum (BICs) in two- or multi-qubit systems lies at the heart of entanglement generation and harnessing in Waveguide Quantum Electrodynami…
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…