Journal of Artificial Intelligence and Capsule Networks
IRO Journals
Volume 8 — Issue 1 — March 2026

Quantum Machine Learning Approaches for High-Dimensional Optimization Problems: A Survey

https://doi.org/10.36548/jaicn.2026.1.001
Pages: 1-18
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Joy Iong-Zong Chen
Professor, Department of Electrical Engineering, Da-Yeh University, Taiwan
Wen-Bing Wu
Yu Da University of Science and Technology, Department of Applied Intelligent Mechanical and Electrical Engineering (AIMEE), Taiwan
March 2026
Volume 8 — Issue 1
Journal of Artificial Intelligence and Capsule Networks
PDF

How to Cite

Chen, Joy Iong-Zong, and Wen-Bing Wu. 2026. “Quantum Machine Learning Approaches for High-Dimensional Optimization Problems: A Survey”. Journal of Artificial Intelligence and Capsule Networks 8 (1): 1-18. https://doi.org/10.36548/jaicn.2026.1.001.
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Keywords

— Quantum Machine Learning
— Quantum Computing
— Quantum Neural Networks
— Variational Quantum Circuits
— Quantum Data Encoding
— Artificial Intelligence
Published: 17-03-2026

Abstract

Recent advances in Quantum Machine Learning (QML) bring together Quantum Computing and Machine Learning with the aim of enhancing the advantages of Machine Learning to solve problems efficiently. The properties of quantum systems (superposition, entanglement, etc.) to represent multiple states of information at once, QML can potentially offer significant advantages when solving complex high-dimensional (multidimensional) computational problems. This paper reviews the current state-of-art regarding QML in general through an organized compilation and assessment of selected studies recently published in scientific literature that were found via large electronic library catalogs based on peer-reviewed sources according to algorithms used, optimization approaches employed, and areas of applied study. This review provides comparative analyses of each selected studies for purposes of identifying major trends in QML research/practice as well as opportunities for methodological improvements and challenges still existing within QML. Finally, this review presents an overview of Quantum Computing and Quantum Data Encoding Principles utilized to translate Classical Data into Quantum States as a high-level evaluation of several popular Quantum Learning Algorithms (e.g., Quantum Neural Networks, Quantum Support Machines, and Variational Quantum Circuit). The survey describes the optimization strategies such as gradient based techniques, Bayesian Optimization and evolutionary techniques being used to improve the stability of the quantum machine learning (QML) training process. Then, it provides examples of application areas for such QML, where healthcare analytics, environmental modelling, manufacturing optimization and analysis of biologic data are included. The comparative review of the recent academic research has identified several limitations and challenges regarding the use of QML that researchers. Although QML is believed to be capable of solving complex computational problems, issues such as noise in hardware, limited numbers of qubits and scalability present significant challenges for researchers working with this type of machine learning application. Overall, it appears that this survey provides a broad overview of contemporary developments and future trends for developing high-performance quantum learning systems.

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