# Advancements and Challenges in Quantum Machine Learning for Medical Image Classification: A Comprehensive Review

Source: [Advancements and Challenges in Quantum Machine Learning for Medical Image Classification: A Comprehensive Review](<https://www.sota2.com/research/paper/68fa4a211e209131ce93c0eb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:54:56.461Z

Authors: Md Farhan Shahriyar, Gazi Tanbhir

Published: 2025-03-23T16:10:54.000Z

## Abstract

Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine Learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve speed and accuracy in disease detection and diagnosis. This paper provides an overview of recent studies on medical image classification through a structured taxonomy, highlighting key contributions, limitations and gaps in current research. It emphasizes moving from simulations to real quantum computers, addressing challenges like noisy qubits and suggests future research to enhance medical image classification using quantum technology.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.13910>)

[PDF](<https://arxiv.org/pdf/2504.13910.pdf>)