AUTHOR=Guddanti Sai Sakunthala , Padhye Apurva , Prabhakar Anil , Tayur Sridhar 

TITLE=Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 5 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1286657

DOI=10.3389/fcomp.2023.1286657

ISSN=2624-9898

ABSTRACT=Early diagnosis of pneumonia is crucial to increase the chances of survival and to reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine-learning tool that can accurately classify images as belonging to normal or infected individuals. 
A support vector machine (SVM) is  
attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing – classical, quantum and hybrid – an attractive approach to explore.

In this paper, we offer a comparison between different methods: (1) \textcolor{blue}{a classical state-of-the-art implementation of SVM (LibSVM);} (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds, using both simulating annealing and quantum annealing. We find that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, \textcolor{blue}{competitive with LibSVM}, and superior to Gurobi and quantum annealing.