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Quantum Machine Learning for Optimisation: A Domain Focused Survey

Authors:

Surya Teja Darbhamalla, Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

International Conference on Modern Artificial Intelligence and Data Science Systems

Abstract

Quantum machine learning (QML) is increasingly tested as a way to tackle optimisation-heavy learning workloads that stretch classical hardware. We review recent research across parameterised circuits, quantum approximate optimisation, annealing-based heuristics, and hybrid generative models to understand how optimisation performance changes when quantum resources enter the loop. The survey traces three threads: where QML currently shows tangible advantages over advanced classical solvers, which algorithmic design patterns improve trainability on noisy devices, and how benchmarking and software stacks must evolve to make those gains repeatable. Throughout the paper, we group findings by application domain and close with deployment guidance that links problem structure to the most promising QML optimisers.

Bibtex

@inproceedings{Darbhamalla7313,
author = {Surya Teja Darbhamalla and Shahina Begum and Shaibal Barua and Mobyen Uddin Ahmed},
title = {Quantum Machine Learning for Optimisation: A Domain Focused Survey},
month = {May},
year = {2026},
booktitle = {International Conference on Modern Artificial Intelligence and Data Science Systems},
url = {http://www.es.mdu.se/publications/7313-}
}