Quantum-inspired Minimum Distance Classification in Biomedical Context

TL;DR

This paper introduces a quantum-inspired modification of the minimum distance classifier and applies it to predict patient survival in idiopathic pulmonary fibrosis, demonstrating its effectiveness in a biomedical setting.

Contribution

It presents a novel quantum-inspired classifier based on the NMC and applies it to a real-world biomedical classification problem.

Findings

Quantum-inspired classifier performs comparably or better than traditional NMC.

Application to IPF survival prediction demonstrates practical utility.

Shows potential for quantum-inspired methods in biomedical data analysis.

Abstract

We face the problem of pattern classification by proposing a quantum-inspired version of the widely used minimum distance classifier (i.e. the Nearest Mean Classifier (NMC)) already introduced in [31,33,28,27] and by applying this quantum-inspired classifier in a biomedical context. In particular, we show and compare the NMC and our quantum model performance to solve a problem related to classify the probability of survival for patients affected by idiopathic pulmonary fibrosis (IPF).