Journal of Multiscale Materials Informatics

Systematic Review of AppSheet-Based Attendance Systems: Outcomes, Features, and Sectoral Adoption

2025-09-07T05:25:42+00:00

The digitalization of organizational processes has accelerated the adoption of efficient attendance management systems. This study systematically reviews AppSheet-based digital attendance implementations, focusing on outcomes, features, and sectoral adoption. Following a PRISMA-guided systematic literature review, 23 studies published between 2020 and 2025 were analyzed. Findings indicate that AppSheet enhances efficiency, accuracy, transparency, and user satisfaction, primarily through mobile access, GPS-based geolocation, QR code verification, and cloud-based reporting. Selfie photo with GPS emerged as the most common feature, followed by QR code–based attendance, while additional functionalities such as digital signatures and form-based attendance provide complementary solutions. Adoption is concentrated in the education sector, particularly primary and secondary schools, with limited uptake in government and corporate contexts. Challenges include scalability limits in free versions, proxy attendance risks, internet dependency, and storage constraints. The study underscores AppSheet’s flexibility as a no-code platform, offering practical, cost-effective, and adaptive solutions for digital attendance management across diverse organizational settings.

Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review

2025-10-16T05:44:19+00:00

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

Quantum Neural Network in Architectures, Learning Mechanisms, and Emerging Applications Across Domains: A Review

2025-10-15T06:24:04+00:00

Quantum Neural Networks (QNNs) represent a novel computational paradigm that merges the principles of quantum computing with the architecture of artificial neural networks. Through the quantum phenomena of superposition, entanglement, and interference, QNNs enable parallel computation in high-dimensional Hilbert spaces, offering the potential to surpass the representational limits of classical models. This review provides a comprehensive overview of the theoretical foundations and architectures of QNNs, including Quantum Perceptrons, Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Recurrent Neural Networks (QRNNs). Furthermore, it discusses hybrid quantum–classical training mechanisms and key challenges such as barren plateaus, decoherence, and sampling complexity. The review also highlights recent applications of QNNs in medical diagnostics, materials science, and financial forecasting, demonstrating their potential to accelerate computation and improve predictive accuracy. Finally, future research directions are discussed in relation to computational efficiency, model interpretability, and integration with next-generation quantum hardware.

Framework for Early Prediction of Lithium-Ion Battery Lifetime: A Hybrid Quantum-Classical Approach

2025-11-10T06:57:46+00:00

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.