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All Journal METIK JURNAL Journal of Multiscale Materials Informatics
Wahyu Aji Eko Prabowo
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review Muhamad Akrom; Aprilyani Nur Safitri; Novianto Nur Hidayat; Wahyu Aji Eko Prabowo; Setyo Budi
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15955

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.
Prediksi Aksebilitas Molekul Tamu pada Metal-Organic Framework dengan SMOTE dan AdaBoost-Machine Learning Moch Anjas Aprihartha; Harun Al Azies; Wahyu Aji Eko Prabowo; Usman Sudibyo; Ika Puspitasari; Indah Putianik; Fatma Ahardika Nurfaizal
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/45crx119

Abstract

Metal-Organic Frameworks (MOFs) are a special class of organic-inorganic hybrid materials widely known for their regular and periodic crystal structures. MOFs are composed of metal ions or clusters connected by organic linkers that form a three-dimensional lattice-shaped series. The advantage of MOFs is their ability to capture guest molecules in their pores. Based on these capabilities, MOFs can be utilized in various applications such as gas absorption and separation processes, catalysts, and therapeutic compound delivery systems. Currently, in creating new materials, the MOFs synthesis process still applies a conventional trial-and-error approach that has the potential for high failure rates. The purpose of this study is to develop a machine learning model as an efficient tool design in creating new MOFs materials before the experimental process is carried out. This study implements the SMOTE and AdaBoost methods integrated with machine learning algorithms in classifying MOFs pores based on the pore limiting diameter (PLD) size. The results obtained from the CART-Gentle AdaBoost model provide the best performance with an accuracy of 72.82%; precision 71.32%; recall 73.53%; specificity 72.88%; and f1 score 72.39%. This model is quite suitable for use in identifying MOF structures that are accessible to guest molecules compared to other classification models.
Co-Authors Aprilyani Nur Safitri Fatma Ahardika Nurfaizal Harun Al Azies Ika Puspitasari Indah Putianik Moch Anjas Aprihartha Muhamad Akrom Novianto Nur Hidayat Setyo Budi Usman Sudibyo