# Image Classification via Quantum Machine Learning

Source: [Image Classification via Quantum Machine Learning](<https://www.sota2.com/research/paper/68fa3dbe1e209131ce8e2c4b>)

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

Data updated: 2025-10-25T01:40:58.430Z

Authors: H\\'ector Iv\\'an Garc\\'ia Hern\\'andez, Raymundo Torres Ruiz, Guo-Hua Sun

Published: 2020-11-03T05:12:51.000Z

## Abstract

Quantum Computing and especially Quantum Machine Learning, in a short period of time, has gained a lot of interest through research groups around the world. This can be seen in the increasing number of proposed models for pattern classification applying quantum principles to a certain degree. Despise the increasing volume of models, there is a void in testing these models on real datasets and not only on synthetic ones. The objective of this work is to classify patterns with binary attributes using a quantum classifier. Specially, we show results of a complete quantum classifier applied to image datasets. The experiments show favorable output while dealing with balanced classification problems as well as with imbalanced classes where the minority class is the most relevant. This is promising in medical areas, where usually the important class is also the minority class.

## 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/2011.02831>)

[PDF](<https://arxiv.org/pdf/2011.02831.pdf>)