# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1904.09185