Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review

Authors

  • Muhamad Akrom Universitas Dian Nuswantoro
  • Aprilyani Nur Safitri Universitas Dian Nuswantoro
  • Novianto Nur Hidayat Universitas Dian Nuswantoro
  • Wahyu Aji Eko Prabowo Universitas Dian Nuswantoro
  • Setyo Budi Universitas Dian Nuswantoro

DOI:

https://doi.org/10.62411/jimat.v3i1.15955

Keywords:

Quantum Machine Learning, Variational Quantum Circuits , Quantum Neural Networks, Quantum Support Vector Machine, Quantum Data Encoding, NISQ

Abstract

Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.

Author Biography

Muhamad Akrom, Universitas Dian Nuswantoro

Scopus: 58054974800 Google Scholar:  HqilwgYAAAAJ Sinta: 6741450  

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Published

2026-05-06

Issue

Vol. 3 No. 1 (2026): April (In Progress)

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Articles

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