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All Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Informatika (JUPITA) SWAGATI: Journal of Community Service Journal of Multiscale Materials Informatics
Gustina Alfa Trisnapradika
Universitas Dian Nuswantoro
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Other

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Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review Gustina Alfa Trisnapradika; Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15154

Abstract

Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.
Co-Authors Achmad Wahid Kurniawan Aprilyani Nur Safitri Bagas Aditya Mahendra Cornellius Adryan Putra Sumarjono Dzaki Asari Surya Putra Filmada Ocky Saputra Harun Al Azies Lusi Noviani Prasetyo Mirza Yuansyah Muhamad Akrom Muhammad Naufal Muhammad Rifki Ardian Muljono, - Mustafid Kaisalana Nico Ananda Febrian Noor Ageng Setiyanto, Noor Ageng Novianto Nur Hidayat Primavieri Rhesa Ardana Renjiro Azhar Pramono Rizky Syah Gumelar Rizky Syah. Gumelar Saraswati, Galuh Wilujeng Setyo Budi Sri Winarno SUPRIADI RUSTAD Usman Sudibyo Wahyu Aji Eko Prabowo Wildanil Ghozi Yudha Mulyana Yuminah Yuminah