We introduce the Tree Tensor Network-enhanced Quantum-Classical Neural Network (TTN-QNet), a hybrid architecture that leverages the hierarchical structure of Tree Tensor Networks for efficient parameter representation and Variational Quantum Circuits (VQC) for expressive modeling. Unlike Tensor Ring Networks, TTNs reduce parameter redundancy through a tree-based topology, enabling scalable and interpretable computation. The proposed TTN-QNet is evaluated on the Iris, MNIST, and CIFAR-10 datasets, achieving classification accuracies of 93.2%, 85.24%, and 81.67%, respectively, on binary classification tasks. TTN-QNet demonstrates rapid convergence and robustness against barren plateaus, offering a promising direction for deep quantum learning.

The rapid advancement of quantum computing poses a substantial threat to classical cryptographic systems, accelerating the global shift toward post-quantum cryptography (PQC). Despite their theoretical robustness, practical deployment of PQC algorithms remains hindered by challenges such as computational overhead, side-channel vulnerabilities, and poor adaptability to dynamic environments. This study integrates machine learning (ML) techniques to enhance three representative PQC algorithms: FrodoKEM, Falcon, and Supersingular Isogeny Key Encapsulation (SIKE). ML is employed for four key purposes: performance optimization through Bayesian and evolutionary parameter tuning; real-time side-channel leakage detection using deep learning models; dynamic algorithm switching based on runtime conditions using reinforcement learning; and cryptographic forensics through anomaly detection on vulnerable implementations. Experimental results demonstrate up to 23.6% reduction in key generation time, over 96% accuracy in side-channel detection, and significant gains in adaptability and leakage resilience. ML models also identified predictive patterns of cryptographic fragility in the now-broken SIKE protocol. These findings confirm that machine learning augments performance and security and enables intelligent and adaptive cryptographic infrastructures for the post-quantum era.

This study investigates the performance of classical, quantum, and hybrid classical-quantum stacking models in predicting Corrosion Inhibition Efficiency (IE%) using 14 QSAR descriptors. The hybrid model combines a Gradient Boosting Regressor (GBR) and a Quantum Support Vector Regressor (QSVR) through a meta-learner (Ridge Regression). Results show a significant improvement over traditional models. The hybrid stacking model achieved an R² of 0.834, an MSE of 8.123, an MAE of 2.371, and an RMSE of 2.850, outperforming both individual classical and quantum models. These results confirm the strength of hybrid models in capturing both complex nonlinear and quantum-interaction patterns in QSAR-based molecular prediction.

This study evaluates gate-based quantum machine learning (QML) models, including the Variational Quantum Classifier (VQC) and Quantum k-Nearest Neighbors (QkNN), on the QM9 quantum chemistry dataset for binary classification of molecular electronic properties. Using IBM Qiskit, both models were tested on simulators and real quantum hardware. Classical models (LightGBM, SVM, MLP) served as benchmarks. Results show classical models outperform quantum ones, with LightGBM achieving the highest AUC-ROC (0.901). However, VQC on simulators achieved a competitive AUC of 0.781, and real hardware still yielded performance above that of chance. Despite hardware constraints, quantum models demonstrated learning capability. The findings support hybrid quantum-classical systems as a promising near-term approach while quantum hardware continues to evolve

The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies

This study aims to examine the relationship between science and religion through two main paradigms, namely the Andromeda Spiral Paradigm from UIN Suska Riau and the Wheel of Revelation Guiding Science Paradigm from UIN Sunan Gunung Djati Bandung. This study was conducted to meet the need for integrative understanding that combines science and spiritual values in facing the challenges of a complex era. The method used is descriptive qualitative with data collection techniques through documentation studies, literature reviews, and analysis of relevant cases related to the application of both paradigms. Data analysis was carried out interpretively and comparatively to understand how these paradigms integrate science and religion within the framework of Islamic science. The results of the study show that both paradigms have succeeded in building a holistic conceptual framework, where science and religion are positioned as two complementary aspects, not contradictory. The Andromeda Spiral Paradigm emphasizes the development of knowledge continuously and dynamically, while the Wheel of Revelation Paradigm emphasizes the importance of revelation as the center of guiding science. Both together form a strong integration model between intellectual and spiritual aspects in Islamic education. In conclusion, the Andromeda Spiral and Wheel of Revelation paradigms can be used as a basis for developing an integrative scientific approach in Islamic universities. Further research is needed to examine the practical implementation of these two paradigms in curriculum development and cross-disciplinary studies to strengthen the synergy between science and religion in the academic environment.

This study investigates the strategic role of Human Resource Management (SHRM) in building Organizational Resilience (OR), particularly through the moderating effect of Transformational Leadership (TL) and the contextual influence of organizational culture. The research addresses a pressing problem: how organizations can maintain adaptability and continuity amid persistent uncertainty and crisis. The objective is to propose and validate an integrative conceptual model linking SHRM practices, leadership behavior, and employee engagement to resilience outcomes. A qualitative exploratory case study design was employed, involving semi structured interviews with senior HR professionals and organizational leaders from multiple industries that have experienced disruption in the past five years. Thematic analysis was applied to identify patterns and validate findings through triangulation and participant feedback. The results reveal that SHRM practices such as talent agility, flexible work arrangements, and leadership development significantly enhance resilience by fostering proactive employee engagement. Furthermore, the presence of transformational leadership strengthens the implementation of these HR strategies, while organizational culture moderates the extent to which engagement translates into resilient behavior. This study contributes to the literature by offering a systemic framework that synthesizes strategic, behavioral, and contextual dimensions of resilience. The proposed model underscores the importance of aligning HR strategies with adaptive leadership and supportive culture to build organizational systems capable of sustaining performance under pressure. These findings have practical implications for HR policymakers and organizational leaders seeking to embed resilience into workforce strategies and institutional frameworks.

Sekolah merupakan sarana utama dalam membentuk sikap dan perilaku individu melalui pendidikan yang terencana. Pendidikan yang berkualitas tidak hanya menghasilkan lulusan yang kompetitif, tetapi juga bermoral, berakar budaya, dan mampu mengelola sumber daya secara optimal. Penelitian ini bertujuan untuk mengkaji manajemen kelas dan efektivitas pembelajaran di MAN 4 Jombang serta hubungan keduanya. Penelitian ini menggunakan pendekatan kualitatif deskriptif dengan teknik observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa manajemen kelas di MAN 4 Jombang mencakup perencanaan pembelajaran, pengaturan ruang, penegakan disiplin, dan penciptaan suasana belajar yang kondusif, dengan guru berperan sebagai fasilitator dan motivator. Efektivitas pembelajaran ditandai dengan partisipasi aktif siswa, peningkatan hasil belajar, dan penggunaan metode yang sesuai dengan karakteristik peserta didik. Manajemen kelas yang efektif terbukti memberikan kontribusi besar terhadap keberhasilan pembelajaran, melalui terciptanya lingkungan belajar yang tertib, menyenangkan, dan mendukung tanggung jawab serta prestasi siswa. Temuan ini menegaskan pentingnya profesionalisme guru dalam menerapkan manajemen kelas sebagai strategi peningkatan mutu pendidikan.

This study investigates the effectiveness of gamification in enhancing English language learning outcomes among learners. Gamification, defined as the integration of game elements such as points, badges, leaderboards, and challenges into non-game settings, has gained widespread popularity as an innovative and motivating instructional approach in education. The primary goal of this research is to evaluate how gamified strategies impact learner engagement, motivation, and overall language proficiency in English classrooms. To achieve this, a quasi-experimental design was employed, involving two groups of learners: an experimental group exposed to gamified learning activities and a control group receiving traditional instructional methods. Data collection included pre- and post-assessments on language skills, as well as surveys measuring learners’ motivation and engagement levels. Analysis of the collected data reveals that the group learning through gamification demonstrated significantly higher gains in language proficiency compared to the control group. Additionally, the gamified group reported enhanced motivation and participation, indicating that gamification positively influences learners’ attitudes toward language learning. These findings suggest that incorporating game elements into English language teaching can create more dynamic, interactive, and enjoyable learning environments. Gamification not only fosters cognitive development but also satisfies psychological needs such as autonomy, competence, and relatedness, which are essential for sustaining motivation. Therefore, this study supports the use of gamified pedagogy as a promising tool to improve educational outcomes, particularly in language acquisition. Future research is encouraged to explore long-term effects and optimal design strategies for gamified learning applications.

The library plays a crucial role in supporting the educational process in schools, particularly as a source of information and knowledge for students. However, in the era of globalization, students' interest in visiting the library is facing challenges, necessitating appropriate managerial strategies to keep the library relevant and appealing. This study aims to analyze library management in increasing students' interest in visiting the library at SMA A Wahid Hasyim Tebuireng Jombang. This research employs a qualitative approach with a case study method. Data collection techniques include observation, interviews, and documentation, while data analysis involves data reduction, data display, and conclusion drawing. The validity of the data is ensured through source triangulation, time triangulation, and technique triangulation. The findings reveal that library management, which includes planning, organizing, implementation, and supervision, significantly contributes to increasing students' interest in visiting the library. Strategies such as updating book collections, creating a comfortable space, implementing literacy programs, and Adequate facilities are highly effective in attracting students' interest. The implications of these findings indicate that effective library management can create an enjoyable learning environment and enhance the literacy culture among students.