In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

Using a quantum processor to embed and process classical data enables the generation of
correlations between variables that are inefficient to represent through classical …

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network
(QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes …

In this paper, we use the quantum Jensen–Shannon divergence as a means of measuring
the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In …

The development of scanning probe microscopy (SPM) has enabled unprecedented
scientific discoveries through high-resolution imaging. Simulations and theoretical analysis …

The convolution operator at the core of many modern neural architectures can effectively be
seen as performing a dot product between an input matrix and a filter. While this is readily …

In this paper, we propose a novel framework of computing the Quantum-based Entropic
Representations (QBER) for un-attributed graphs, through the Continuous-time Quantum …

The von Neumann entropy of a graph is a spectral complexity measure that has recently
found applications in complex networks analysis and pattern recognition. Two variants of the …

Network representations are powerful tools to modeling the dynamic time-varying financial
complex systems consisting of multiple co-evolving financial time series, eg, stock prices. In …

The computer-aided disease diagnosis from radiomic data is important in many medical
applications. However, developing such a technique relies on labeling radiological images …