# A Quantum-Classical Hybrid Method for Image Classification and Segmentation

Source: [A Quantum-Classical Hybrid Method for Image Classification and Segmentation](<https://www.sota2.com/research/paper/68fa40961e209131ce8f4aae>)

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

Data updated: 2025-10-25T02:52:42.656Z

Authors: Sayantan Pramanik, M Girish Chandra, C V Sridhar, Aniket Kulkarni, Prabin Sahoo, Vishwa Chethan D V, Hrishikesh Sharma, Ashutosh Paliwal, Vidyut Navelkar, Sudhakara Poojary, Pranav Shah, Manoj Nambiar

Published: 2021-09-25T13:18:47.000Z

## Abstract

Enormous activity in the Quantum Computing area has resulted in considering them to solve different difficult problems, including those of applied nature, together with classical computers. An attempt is made in this work to nail down a pipeline consisting of both quantum and classical processing blocks for the task of image classification and segmentation in a systematic fashion. Its efficacy and utility are brought out by applying it to Surface Crack segmentation. Being a sophisticated software engineering task, the functionalities are orchestrated through our in-house Cognitive Model Management framework.

## 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/2109.14431>)

[PDF](<https://arxiv.org/pdf/2109.14431.pdf>)