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All Journal Eksergi: Chemical Engineering Journal SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Renewable Energy and Mechanics International Journal of Advances in Data and Information Systems JoMMiT : Jurnal Multi Media dan IT Environmental Science and Engineering Conference Proceeding Lok Seva: Journal of Contemporary Community Service Journal of Computing Theories and Applications Jurnal Pengabdian Masyarakat Nusantara Journal of Multiscale Materials Informatics Advance Sustainable Science, Engineering and Technology (ASSET)
Muhamad Akrom
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review Dian Arif Rachman; 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.v2i2.14935

Abstract

Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applications
Framework for Early Prediction of Lithium-Ion Battery Lifetime: A Hybrid Quantum-Classical Approach Sheilla Rully Anggita; 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.v2i2.15055

Abstract

Accurately predicting the lifetime of lithium-ion batteries during early charge–discharge cycles remains a significant challenge due to the nonlinear and weakly expressed degradation dynamics in the initial stages of operation. Classical machine learning (ML) models—although effective in pattern recognition—often face limitations in modeling complex correlations within small, high-dimensional datasets. To address these challenges, this study proposes a Hybrid Quantum–Classical Machine Learning (HQML) framework that integrates a Variational Quantum Circuit (VQC) as a quantum feature encoder with a Gradient Boosting Regressor (GBR) as the classical learner. The proposed approach is implemented using the Qiskit Aer simulator on the MIT Battery Degradation Dataset (124 cells, 42 engineered features). By encoding multi-source degradation descriptors (voltage, capacity, temperature, internal resistance) into Hilbert space via amplitude and angle encoding, the HQML model captures intricate nonlinear feature interactions that are inaccessible to conventional kernels. Experimental results demonstrate that the hybrid model achieves an RMSE of 93 cycles and an R² of 0.94, outperforming the best classical baseline (SVM + Wrapper selection, RMSE = 115, R² = 0.90). Furthermore, quantum observables analysis reveals interpretable correlations between entanglement strengths and physical degradation indicators. These results highlight the potential of quantum machine learning as a powerful paradigm for high-fidelity battery prognostics in the early-life regime.
Hybrid Quantum Neural Network for Predicting Corrosion Inhibition Efficiency of Organic Molecules Wise Herowati; 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.v2i2.15132

Abstract

Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics.
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.
A novel quantum circuit-based adaptive quantum convolutional neural network for image classification Reza Pamungkas Putra Sukanli; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 3 No. 2 (2026): October (In Progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i2.17109

Abstract

This study explores Quantum Convolutional Neural Network (QCNN) starting from foundational quantum operations, such as the Rx gate for encoding MNIST image data into quantum states. We implemented quantum convolutional and pooling layers using one_unitary and two_unitary circuits, enabling effective feature extraction and dimensionality reduction while preserving critical information. Expressibility analysis revealed varying capabilities across different one_unitary circuits, with Rx, Ry, and Rz combinations demonstrating promising results akin to Haar random states. The proposed QCNN model exhibited robust performance metrics (accuracy: 95.98%, precision: 94.44%, recall: 96.59%, F1-score: 0.9551, AUC: 0.9604) in classification tasks, supported by efficient convergence during optimization. Future directions include expanding QCNN applications to handle more complex datasets and optimizing architectures to enhance quantum machine learning capabilities, particularly in image processing. This study underscores the potential of QCNNs in advancing quantum computing applications in neural network architectures.
A Robust Voting Ensemble Framework for Predicting Thermal Stability in Zn-Based Metal–Organic Frameworks Taufiqul Umam; Harun Al Azies; Muhamad Akrom; Ananta Surya Pratama; Muhammad Diva Irnanda
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12748

Abstract

The prediction of thermal stability (TS) in zinc-based metal–organic frameworks (Zn-MOFs) is often challenged by experimental cost and distributional heterogeneity in materials datasets. This study proposes a median-based robust voting ensemble to model the TS of 151 Zn-MOF samples using four structural descriptors, nN, nZn, Het, and Lig. The framework integrates five robust linear estimators and is benchmarked against a linear kernel Support Vector Regression (SVR) model to evaluate predictive stability and generalization performance. The proposed ensemble demonstrates superior test performance (R² = 0.9986; RMSE = 0.0023) compared to SVR (R² = 0.9492; RMSE = 0.0213), indicating enhanced robustness under heterogeneous data conditions. Feature importance analysis identifies nitrogen coordination density and heteroatomic environment as the dominant contributors to TS prediction, while zinc center quantity and ligand topology exhibit comparatively minor influence. These findings confirm that median-based robust aggregation improves predictive reliability and provides chemically interpretable insight, offering a data-driven approach for the rational design and screening of thermally stable Zn-MOF materials.
Pelatihan Bijak Gunakan AI dalam Praktik Penulisan Prompt untuk Software Development Siswa Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom; Didik Hermanto
Jurnal Pengabdian Masyarakat Nusantara Vol 5 No 2 (2026): Juni 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/dimastara.v5i2.28674

Abstract

Perkembangan teknologi Artificial Intelligence (AI) yang semakin pesat memberikan dampak signifikan dalam berbagai bidang, termasuk dalam dunia pendidikan dan pengembangan perangkat lunak (software development). Namun, pemanfaatan AI secara bijak dan efektif masih menjadi tantangan bagi siswa, khususnya dalam memahami cara berinteraksi dengan sistem AI melalui penulisan prompt yang tepat. Oleh karena itu, kegiatan Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan dengan sasaran siswa SMK Negeri 9 Semarang melalui program “Pelatihan Bijak Gunakan AI: Meningkatkan Pemahaman dan Praktik Penulisan Prompt dalam Software Development”. Kegiatan ini bertujuan untuk meningkatkan literasi AI serta kemampuan siswa dalam menyusun prompt yang efektif untuk mendukung proses pemrograman dan pengembangan perangkat lunak. Metode pelaksanaan kegiatan meliputi beberapa tahapan, yaitu penyampaian materi mengenai konsep dasar AI dan etika penggunaannya, pengenalan teknik penulisan prompt yang baik, diskusi interaktif, praktik langsung penggunaan AI dalam membantu proses software development, serta sesi pendampingan bagi peserta. Melalui kegiatan pelatihan ini diharapkan siswa dapat memahami prinsip penggunaan AI secara bijak, mampu menyusun prompt yang lebih terstruktur dan efektif, serta memanfaatkan AI sebagai alat bantu pembelajaran dan pengembangan keterampilan pemrograman. Hasil kegiatan menunjukkan bahwa pelatihan ini dapat meningkatkan pemahaman siswa terhadap penggunaan AI secara produktif dan bertanggung jawab dalam mendukung proses belajar di bidang teknologi informasi.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Akbar Priyo Santosa Ananta Surya Pratama Aprilyani Nur Safitri Ayu Pertiwi De Rosal Ignatius Moses Setiadi Diah Rahayu Ningtias Dian Arif Rachman Dian Arif Rachman Didik Hermanto Gustina Alfa Trisnapradika Gustina Alfa Trisnapradika Harun Al Azies Khafiizh Hastuti Lubna Mawaddah Moch. Anjas Aprihartha Muhammad Diva Irnanda Muhammad Reesa Muhammad Reesa Rosyid Noor Ageng Setiyanto, Noor Ageng Novianto Hidayat Novianto Nur Hidayat Purwanto Purwanto Reza Pamungkas Putra Sukanli Setyo Budi Sheilla Rully Anggita Taufiqul Umam Totok Sutojo Totok Sutojo Usman Sudibyo Wahyu Aji Eko Prabowo Wahyu Aji Eko Prabowo Wise Herawati Wise Herowati