Radial basis function network using Lambert-Tsallis Wq function

TL;DR

This paper introduces a novel radial basis function network utilizing the Lambert-Tsallis Wq function as a kernel, applied to quantum state discrimination and probability density estimation, demonstrating its effectiveness in quantum information processing.

Contribution

The work presents a new RBFN kernel based on the Lambert-Tsallis Wq function, applied to quantum state classification and density estimation, expanding RBFN applications in quantum computing.

Findings

Effective discrimination between entangled and disentangled qubit states.

Successful estimation of probability density functions using the proposed kernel.

Demonstrated applicability of Lambert-Tsallis Wq function in quantum data analysis.

Abstract

The present work brings two applications of the Lambert-Tsallis Wq function in radial basis function networks (RBFN). Initially, a RBFN is used to discriminate between entangled and disentangled bipartite of qubit states. The kernel used is based on the Lambert-Tsallis Wq function for q = 2 and the quantum relative disentropy is used as distance measure between quantum states. Following, a RBFN with the same kernel is used to estimate the probability density function of a set of data samples.