Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases

TL;DR

This paper introduces QAMDiagnos, a quantum associative memory model combining linear and non-linear algorithms to aid in rapid, accurate, and low-cost diagnosis of tropical diseases with similar symptoms, suitable for medical staff without extensive laboratory facilities.

Contribution

It presents a novel quantum associative memory model integrating linear and non-linear algorithms for diagnosing tropical diseases, enhancing accuracy and usability.

Findings

High recognition efficiency with disease symptoms input

Non-linear algorithm confirms or corrects diagnosis

Model is user-friendly and suitable for low-resource settings

Abstract

This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linear algorithm is the main algorithm. The non-linear algorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases.