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Coecke and Duncan recently introduced a categorical formalisation of the interaction of complementary quantum observables. In this paper we use their diagrammatic language to study graph states, a computationally interesting class of…

Quantum Physics · Physics 2013-06-20 Ross Duncan , Simon Perdrix

Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the appearance of Graph Transformers using global graph…

Quantum Physics · Physics 2022-10-20 Slimane Thabet , Romain Fouilland , Loic Henriet

This paper investigates the relationship between categorical entropy and von Neumann entropy of quantum lattices. We begin by studying the von Neumann entropy, proving that the average von Neumann entropy per site converges to the logarithm…

Statistical Mechanics · Physics 2025-05-27 Haiqi Wu , Kai Xu

The spectral properties of the Laplacian on a class of quantum graphs with random metric structure are studied. Namely, we consider quantum graphs spanned by the simple $\ZZ^d$-lattice with $\delta$-type boundary conditions at the vertices,…

Mathematical Physics · Physics 2009-11-13 Frédéric Klopp , Konstantin Pankrashkin

Cardiac blood flow patterns contain rich information about disease severity and clinical interventions, yet current imaging and computational methods fail to capture underlying relational structures of coherent flow features. We propose a…

We present a comprehensive extension of the latent position network model known as the random dot product graph to accommodate multiple graphs -- both undirected and directed -- which share a common subset of nodes, and propose a method for…

Machine Learning · Statistics 2021-01-26 Andrew Jones , Patrick Rubin-Delanchy

Evolution of charged quantum fields under the action of constant nonuniform electric fields is studied. To this end we construct a special generating functional for density operators of the quantum fields with different initial conditions.…

High Energy Physics - Theory · Physics 2019-06-25 S. P. Gavrilov , D. M. Gitman , A. A. Shishmarev

We address the Laplacian on a perturbed periodic graph which might not be a periodic graph. We present a class of perturbed graphs for which the essential spectra of the Laplacians are stable even when the graphs are perturbed by adding and…

Mathematical Physics · Physics 2015-10-01 Itaru Sasaki , Akito Suzuki

Nonlinear spectral graph theory is an extension of the traditional (linear) spectral graph theory and studies relationships between spectral properties of nonlinear operators defined on a graph and topological properties of the graph…

Spectral Theory · Mathematics 2025-04-07 Piero Deidda , Francesco Tudisco , Dong Zhang

Recently, Braunstein et al. [1] introduced normalized Laplacian matrices of graphs as density matrices in quantum mechanics and studied the relationships between quantum physical properties and graph theoretical properties of the underlying…

Quantum Physics · Physics 2011-11-15 Chai Wah Wu

The manifold under consideration consists of the faithful normal states on a sigma-finite von Neumann algebra in standard form. Tangent planes and approximate tangent planes are discussed. A relative entropy/divergence function is assumed…

Mathematical Physics · Physics 2023-02-09 Jan Naudts

This work studies how a suitably-designed classical system generates with a quantum-like (QL) state space mediated by a graph. The graph plays a special dual role by directing the topology of the classical network and defining a state space…

Quantum Physics · Physics 2026-03-24 Gregory D. Scholes

Inspired by the notion of action convergence in graph limit theory, we introduce a measure-theoretic representation of matrices, and we use it to define a new notion of pseudo-metric on the space of matrices. Moreover, we show that such…

Combinatorics · Mathematics 2023-01-05 Raffaella Mulas , Giulio Zucal

In this work we study the entropy of the Gibbs state corresponding to a graph. The Gibbs state is obtained from the Laplacian, normalized Laplacian or adjacency matrices associated with a graph. We calculated the entropy of the Gibbs state…

Probability · Mathematics 2021-01-12 Adam Glos , Aleksandra Krawiec , Łukasz Pawela

We develop a unified quantum framework for subgraph counting in graphs. We encode a graph on $N$ vertices into a quantum state on $2\lceil \log_2 N \rceil$ working qubits and $2$ ancilla qubits using its adjacency list, with worst-case gate…

Quantum Physics · Physics 2026-04-22 Bibhas Adhikari

This work exploits a framework whereby a graph (in the mathematical sense) serves to connect a classical system to a state space that we call `quantum-like' (QL). The QL states comprise arbitrary superpositions of states in a tensor product…

Quantum Physics · Physics 2026-03-24 Gregory D. Scholes

We investigate quantum graphs with infinitely many vertices and edges without the common restriction on the geometry of the underlying metric graph that there is a positive lower bound on the lengths of its edges. Our central result is a…

Mathematical Physics · Physics 2018-10-30 Pavel Exner , Aleksey Kostenko , Mark Malamud , Hagen Neidhardt

In this paper we investigate the von Neumann entropy in the ground state of one-dimensional anyonic systems with the repulsive interaction. Based on the Bethe-ansatz method, the entanglement properties for the arbitrary statistical…

Quantum Physics · Physics 2014-11-20 Hongli Guo , Yajiang Hao , Shu Chen

We define correlational (von Neumann) entropy for an individual quantum state of a system whose time-independent hamiltonian contains random parameters and is treated as a member of a statistical ensemble. This entropy is representation…

chao-dyn · Physics 2013-01-16 Valentin V. Sokolov , B. Alex Brown , Vladimir Zelevinsky

This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar-like transformation…

Machine Learning · Computer Science 2025-10-02 Theodor-Adrian Badea , Bogdan Dumitrescu