Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study

TL;DR

This study explores the feasibility of integrating a Quantum Neural Network classifier into a cancer registry testing system, demonstrating comparable performance to classical models and providing insights into optimal QNN configurations.

Contribution

It introduces Qlinical, a quantum machine learning approach within EvoMaster for cancer registry system testing, and evaluates its performance and configuration effects.

Findings

Qlinical achieves performance comparable to classical models.

Various QNN configurations impact performance significantly.

Recommendations for optimal QNN settings are provided.

Abstract

The Cancer Registry of Norway (CRN) is a part of the Norwegian Institute of Public Health (NIPH) and is tasked with producing statistics on cancer among the Norwegian population. For this task, CRN develops, tests, and evolves a software system called Cancer Registration Support System (CaReSS). It is a complex socio-technical software system that interacts with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of CaReSS, CRN has employed EvoMaster, an AI-based REST API testing tool combined with an integrated classical machine learning model. Within this context, we propose Qlinical to investigate the feasibility of using, inside EvoMaster, a Quantum Neural Network (QNN) classifier, i.e., a quantum machine learning model, instead of the existing classical machine learning model. Results indicate that Qlinical can achieve performance comparable to that of EvoClass. We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.