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This study aims to analyze the effect of the Quantum Learning model using miniature terrarium media to improve science learning outcomes in grade V of SD Negeri 104202 Bandar Setia. The type of research used is a pre-experimental method with a One Group Pretest-Posttest design . The subjects in this study were 21 students in class VC who were determined through non-probability sampling techniques. The test instrument was in the form of 20 multiple-choice questions that had been analyzed through validity and reliability tests. The data results were analyzed using normality, homogeneity, linearity, simple linear regression, and paired sample t-test tests . The results showed that the average pre-test score was 68.57, increasing to 87.14 in the post-test. The paired sample t-test produced a Sig. value. 0.000 < 0.05 so that H0 is rejected and H1 is accepted, while the regression test obtained an R (R Square) of 0.691, which means that the Quantum Learning model with miniature terrarium media has an effect of 69.1% on improving student learning outcomes. This finding confirms that the Quantum Learning model using terrarium media has an effect on improving student learning outcomes.

Improving students' learning motivation in Natural Sciences (IPA) in fifth grade students of SDI Tabene through the application of the Quantum Teaching learning model. The Quantum Teaching learning model, with the TANDUR principle (Grow, Experience, Name, Demonstrate, Repeat, Celebrate), is designed to create a fun and relevant learning environment to students' experiences. This Classroom Action Research was carried out in two cycles, each consisting of planning, implementation, observation, and reflection. The subjects of the study were 32 fifth grade students of SDI Tabene. Data were collected through observation, tests, and documentation. The results showed an increase in students' learning motivation. In cycle I, the average student learning motivation was 60.7%, which was classified as sufficient. After improvements were made in cycle II by optimizing each stage of TANDUR, the average student learning motivation increased to 82.1%, classified as very good. This increase was seen from students' higher enthusiasm, active participation in discussions, and increased curiosity about science materials. It can be concluded that the application of the Quantum Teaching learning model is effective in improving students' learning motivation in fifth grade students of SDI Tabene.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

. This study examines how the Quantum Learning model is used in the Raudhatul Athfal (RA) An-Nidhomiyah class. RA An-Nidhomiyah is an early childhood education center dedicated to raising a generation of Muslims who are highly moral, intellectual, and independent. An innovative method that makes learning fun and efficient is the form of quantum learning. This model combines interactive principles that encourage different learning styles, actively engage students, and consider the developmental needs of children. For example, singing while memorizing, visiting tours, and telling stories while acting out. The findings of the study indicate that the use of Quantum Learning in RA An-Nidhomiyah improves children's memory and motivation to learn while helping them understand moral and spiritual principles in a supportive atmosphere. This study emphasizes the value of innovation.   Keywords: Innovation Values, Early Childhood Education and Quantum Learning.

This Research aims to: 1) Find out the concept of the Quantum Tahfiz Al-Qur'an method for students of SMA Takhassus Al-Qur'an memorization 2) Find out the implementation of the Quantum Tahfiz Al-Qur'an method for students of SMA Takhassus Al-Qur'an memorization 3) Find out the supporting and inhibiting factors in the implementation of the Quantum Tahfiz Al-Qur'an method. This thesis uses a qualitative approach where the type of research is field research. Data collection techniques use observation, interview, and documentation methods. The analysis techniques used are data reduction, data presentation and drawing conclusions. The results of the study show that; 1) The concept of the Quantum Tahfiz Al-Qur'an memorization method includes a combination of the Quantum Tahfiz Al-Qur'an and traditional methods, activation of the right and left brain, a schedule for studying the Koran that is adjusted to school activities; 2) Implementation of the Qu$antu$m Tahfiz Al-Qu$r'an method includes the activity of reciting the Koran by using the Qu$antu$m Tahfiz Al-Qu$r'an method, the Quantum Tahfiz Al-Qur'an method is very effective for memorizing, using ru$mu$san; 3) The supporting factors are innovative learning, quality teaching or good teaching, a supportive environment, student motivation from coaches and teachers, adequate facilities and infrastructure. Meanwhile, inhibiting factors in the implementation of the Quantum Tahfiz Al-Qur'an method are internal factors such as laziness, lack of motivation, external factors such as situations that are less conducive, friends or parents who are less supportive, and technical factors such as inappropriate student schedules and it's hard to understand the method.

Learning is a process carried out by teachers with their students. In the field, it often occurs rigidly in its implementation and is Teacher Centered so that students are less active in learning activities. The purpose of the study was to determine the impact of the Quantum Learning model on students' critical thinking skills in Class XI IPA 1 MA-Al-Kausar Banjar on the subject of the sensory system. The research time was April 13, 2025. The method used in this study was quasi-experimental. With data collection techniques through pretest and posttest with students with a sample size of 20 people. Based on the results of data analysis using the Z test, it was obtained that Zhitung = 3 and Z Tabel = 2.33, then Zhitung> Z Tabel, meaning that the impact of implementing the Quantum Learning learning model has an effect on students' critical thinking skills on the Sensory System material by 0.74 (high category).

This study aims to identify differences in student learning outcomes between those who receive learning using the Quantum Teaching model and those who receive conventional learning on the material of reaction rates at SMA Negeri 1 Tapa. This study is a quantitative study that applies a quasi-experimental method through a control group design with pretest and posttest. The results of the study indicate that after the application of the Quantum Teaching learning model in the experimental class and the use of the conventional learning model in the control class, there was an increase in student learning outcomes. The average pretest score of the experimental class was 34.37 and the posttest score reached 77.31, while for the control class, the average pretest score was 39 and the posttest score was 58.21. It can be seen that both groups of classes showed significant progress , which can be seen from the higher scores in the experimental group when compared to the control group . The hypothesis was tested through a mean test ( t- test ). The results of the Paired Sample t-test in the experimental group showed a significant difference between the pretest and posttest results, with a significance value of 0.000 , which is much lower than the limit of α = 0.05. This study shows that learning using the Quantum Teaching model has a positive impact on improving students' conceptual understanding.

This research aimed at finding out the effect of Quantum Learning model toward student self-confidence in Social Science learning at Islamic Junior Hight School of Al-Muttaqin Pekanbaru. Two classes, IX.C (experimental) and IX.G (control), were chosen using purposive sampling and a quantitative quasi-experimental design. Observation, surveys, and documentation were used to gather data, and Excel and SPSS 25 were used to analyze the results using t-tests, effect size, and N-Gain. Results indicated that the experimental group's self-confidence (mean = 86.76) was significantly higher than that of the control group (mean = 80.38), with a p-value of 0.000 (< 0.05), a moderate effect size of 0.51, and an N-Gain of 42.33% (moderate) vs 10.66% (low). These results attest to the Quantum Learning model's efficacy in raising students' self-convidence.

This study aims to analyze the level of understanding of PSPK 23 B students at Medan State University on atomic theory questions. With a quantitative descriptive approach, data were collected through multiple-choice tests filled out by 30 students. The results showed an average understanding of 75.33%, which is quite good. However, there was significant variation where some students mastered basic concepts (eg Dalton and Thomson's atomic models), while others had difficulty with abstract topics (eg Bohr's model and quantum mechanics). This finding is in line with previous studies on misconceptions in atomic structure. This study emphasizes the importance of innovative learning strategies, such as visual media and conceptual approaches, to improve student understanding. Periodic evaluation is needed to detect learning gaps and adjust teaching methods. The results of this study can be a basis for developing more effective chemistry education practices.

The advancement of science and technology has significantly impacted the education sector. Quantum Teaching and Quantum Learning emerge as innovative approaches that optimize teacher-student interactions to create effective learning experiences. Quantum Learning emphasizes enjoyable learning, while Quantum Teaching focuses on teaching strategies that build student motivation through suggestive approaches. These methods are developed from various theories, such as Suggestology, Accelerated Learning, and Neuro-Linguistic Programming (NLP). Research findings indicate that implementing Quantum Teaching and Quantum Learning enhances learning motivation, creativity, and academic performance. Therefore, these methods are highly relevant for educational systems to foster a more interactive and enjoyable learning environment.

TANDUR type quantum learning aims to make mathematics more interactive and fun to learn. The main goal of this model is to increase students' desire to learn, improve their writing skills, and create effective communication between teachers and students. Through systematic processes such as Grow, Experience, Name, Demonstrate, Repeat, and Celebrate, students learn about things related to everyday life. Additionally, this approach creates a dynamic learning environment, encourages active participation, and enables meaningful learning. Through a structured and experience-based approach, the TANDUR type quantum learning model is expected to continuously improve students' literacy and writing skills.

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.

In this review, we undertake an in-depth survey of the traditional as well as modern methods used in finding solutions for partial differential equations (henceforth PDEs). We categorise these equations into three main kinds: elliptic, parabolic, and hyperbolic. We also give illustrative examples of these PDEs and discuss the applications of them in a range of fields. This range extends from fluid dynamics (hydrodynamics), as well as thermal (heat) conduction, to quantum mechanics. Our exploration features a number of analyses used in this regard such as variable splitting or defactorising in addition to the transforms invented by Fourier and Laplace. Not only this but also this survey takes in numerical methods ranging from grid-based (finite difference), mesh-based (finite element) to spectral. Also discussed in this paper is a range of special techniques that ranges from the variational techniques, Green's (fundamental solution) functions to perturbation (also known as Asymptotic expansion in addition to sketching the latest developments with respect to computational methods. This review also sheds light on current challenges that confront addressing complicated PDEs especially those nonlinear and multi-variable. In this regard, the paper calls for more research in order to develop more effective methods. The paper maps out the importance of PDE usages in real-life and its potential for more related discoveries in the future particularly with respect to areas such as machine learning and quantum computing.

This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.

The aim of this research is to determine the increase in interest in learning for fourth grade students in science subjects with the topic of discussing the characteristics of plants on Ceremai trees through the Quantum Teaching Learning Model at SDI Duarato for the 2024/2025 academic year. This type of research is Classroom Action Research (CAR). The subjects of this research were 15 grade IV students at SD Inpres Duarato, consisting of 8 male students and 7 female students. The object of this research is Natural Sciences (IPA) learning using the Quantum Teaching learning model. Based on research and data analysis, it can be concluded that the Quantum Teaching learning model can increase interest in learning science in class IV students at SD Inpres Duarato for the 2024/2025 academic year. This can be seen before implementing the Quantum Teaching learning model in science learning. Of the 15 students, 6 students (40%) completed it, and 9 students (60%) did not complete it. Furthermore, by applying the Quantum Teaching learning model, it was obtained that 9 students (60%) had completed their learning interest in cycle I, while 6 students (40%) had not completed it. In cycle II, 13 students (86.66%) completed it, and 2 students (13.33%) did not complete it. From the data above, there was an increase from the initial data, cycle I and cycle II.

This study examines the role of digital simulations in improving university students' understanding of complex physics concepts. Utilizing an experimental design, the research assessed students' conceptual grasp before and after the use of digital simulations in instructional sessions. Results indicated a significant improvement in students' understanding and retention of physics concepts, particularly in areas such as electromagnetism and quantum mechanics. The findings support digital simulation as an effective tool for enhancing conceptual learning in science education.

The rapid advancement of modern computing has driven extensive research on numerical algorithms for solving large-scale systems of linear equations. Classical methods such as LU decomposition, Jacobi, and Gauss–Seidel have been revisited and optimized to leverage parallel architectures, GPUs, and even quantum platforms. Recent studies demonstrate that optimized algorithms can reduce computation time by more than 50% while maintaining high accuracy in solving high-dimensional problems. LU decomposition, particularly in its parallel and GPU-based implementations, has shown superior performance in batch processing and industrial-scale simulations. Meanwhile, iterative methods such as Jacobi and Gauss–Seidel remain relevant due to their flexibility in numerical modeling, with further developments for block matrix systems, finite element applications, and FPGA architectures. The integration of these enhanced algorithms is not only beneficial for the advancement of scientific software development but also supports practical applications in engineering simulations, large-scale data optimization, and machine learning. Therefore, an integrative review of modern numerical algorithm developments is crucial in bridging the gap between industrial demands and research progress in scientific computing.

Quantum computing has emerged as a transformative technology with the potential to revolutionize numerical methods in scientific research. This study explores the integration of quantum algorithms to enhance the efficiency and accuracy of computational techniques used in solving complex scientific problems. The objective of this research is to investigate how quantum computing can address limitations in classical numerical methods, particularly in areas such as optimization, simulation, and data analysis. By employing quantum-enhanced algorithms, such as quantum Monte Carlo and quantum machine learning, the study demonstrates significant improvements in processing speed and solution quality. The findings highlight the capability of quantum computing to tackle challenges in high-dimensional computations and provide novel insights into scientific phenomena. These advancements have profound implications for disciplines ranging from physics and chemistry to material science and beyond, paving the way for a new era of computational-driven discoveries.

Efforts must always be made to improve students' reading abilities in elementary school students, especially as reading, which is part of literacy, is very poor, especially for elementary school students. The quantum reading method is an ideal method to be implemented in elementary schools so that students can continue to increase their interest in reading. The research aims to examine the application of the quantum reading method in elementary schools, both in essence and in theoretical-based applications.This research is based on facts in the field, namely students' low interest in reading, no initiative to read books, lack of reading activities, and unsupportive library facilities. So students often experience difficulties in learning to read, especially in speed reading. So the author took the initiative to describe the analysis of learning difficulties in learning to read quickly for fifth grade elementary school students along with the solutions offered using the quantum reading method.The formulation of the problem in this research is how to increase students' reading interest who use the quantum Reading method and conventional reading methods in Indonesian language subjects in Grade V Elementary Schools. This research aims to determine the increase in reading interest of students who use the quantum reading method and conventional methods in schools. Base.    

Improving the quality of learning is something that must be done well in school education, students who have diverse learning styles must be the main factor of teacher attention in order to be able to give meaning to learning well. To improve learning outcomes, one of them is through the application of the quantum learning model in the teaching and learning process. This research aims to find answers to the question of the effectiveness of using quantum learning in schools. Penalitian conducted by reviewing a variety of relevant literature both from journals and direct findings in the field. The results of the study found that the implementation of quantum learning can improve the quality of learning and maximum student understanding, given the different learning styles of students can be facilitated through the implementation of quantum learning models, so that learning styles are very supportive of quantum learning models and get perfect learning outcomes than conventional learning models.