Quantum computing (QC) and quantum machine learning (QML), two emerging technologies, have the potential to completely change how we approach solving difficult problems in physics and astronomy, among other fields. Potentially Hazardous Asteroids (PHAs) can produce a variety of damaging phenomena that put biodiversity and human life at serious risk. Although PHAs have been identified through the use of machine learning (ML) techniques, the current approaches have reached a point of saturation, signaling the need for additional innovation. This paper provides an in-depth examination of various machine learning (ML) and QML techniques for precisely identifying potentially hazardous asteroids. The study attempts to provide information to improve the efficiency and accuracy of asteroid categorization by combining QML techniques like deep learning with a variety of machine learning (ML) algorithms, such as Random Forest and support vector machines. The study highlights weaknesses in existing approaches, including feature selection and model assessment, and suggests directions for further investigation. The results highlight the significance of developing QML techniques to increase the prediction of asteroid hazards, consequently supporting enhanced risk assessment and space exploration efforts. Paper reviews might not be related because the study only looks at generic paper reviews.
Quantum computing (QC) and quantum machine learning (QML), two emerging technologies, have the potential to completely change how we approach solving difficult problems in physics and astronomy, among other fields. Potentially Hazardous Asteroids (PHAs) can produce a variety of damaging phenomena that put biodiversity and human life at serious risk. Although PHAs have been identified through the use of machine learning (ML) techniques, the current approaches have reached a point of saturation, signaling the need for additional innovation. This paper provides an in-depth examination of various machine learning (ML) and QML techniques for precisely identifying potentially hazardous asteroids. The study attempts to provide information to improve the efficiency and accuracy of asteroid categorization by combining QML techniques like deep learning with a variety of machine learning (ML) algorithms, such as Random Forest and support vector machines. The study highlights weaknesses in existing approaches, including feature selection and model assessment, and suggests directions for further investigation. The results highlight the significance of developing QML techniques to increase the prediction of asteroid hazards, consequently supporting enhanced risk assessment and space exploration efforts. Paper reviews might not be related because the study only looks at generic paper reviews.
Quantum computing (QC) and quantum machine learning (QML) are emerging technologies with the potential to revolutionize the way we approach complex problems in mathematics, physics, and other fields. The increasing availability of data and computing power has led to a rise in using Artificial Intelligence (AI) to solve real-time problems. In space science, employing AI-based approaches to address various challenges, including the potential risks posed by asteroids, is becoming increasingly necessary. Potentially Hazardous Asteroids (PHAs) can cause significant harm to humans and biodiversity through wind blasts, overpressure shock, thermal radiation, cratering, seismic shaking, ejecta deposition, and even tsunamis. Machine Learning (ML) algorithms have been employed to detect hazardous asteroids based on their parameters. Still, there are limitations to the current techniques, and the results have reached a saturation point. To address this issue, we propose a Quantum Machine Learning (QML)-based framework for asteroid hazard prediction, employing Variational Quantum Circuits (VQC) and PegasosQSVC algorithms. The framework aims to leverage the quantum properties of the data to improve the accuracy and precision of asteroid classification. Our study focuses on the impact of PHAs, and the proposed supervised QML-based method aims to detect whether an asteroid with specific parameters is hazardous or not. We compared several classification algorithms and found that the proposed QML-based framework employing VQC and PegasosQSVC outperformed the other methods, with an accuracy of 98.11% and an average F1-score of 92.69%.
Abstract An asteroid impact is one of the rare natural disasters that can be prevented or mitigated using the proper preparation and preparatory measures. The main goal is to investigate the use of quantum machine learning (QML) in the context of asteroid prediction in order to improve early detection and trajectory forecasting capabilities. New computational approaches are necessary in the dynamic field of astronomical hazard assessment, and QML offers itself as an advanced paradigm to meet the challenges of this important task. In this study, we evaluate the EQIE-FCM (Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means) clustering algorithm and compare it with other models such as K-Medoid, Spectral Clustering, Fuzzy C-Means, Quantum K-Means, and Quantum Fuzzy C-Means. EQIE-FCM outperforms these models, surpassing Silhouette and Davies-Bouldin thresholds. The choice of clustering algorithm depends on data characteristics and problem context. By leveraging quantum computing to evolve crucial parameters, EQIE-FCM effectively clusters datasets. We evaluate its efficacy using different-sized asteroid datasets. Quantum machine learning shows promise for accurate predictions of hazardous asteroids, but its integration requires awareness of both strengths and limitations.
This research investigates the use of quantum machine learning (QML) to classify asteroids into non-hazardous and hazardous groups, which yields successful results in detecting the hazard. In addition to the complexity involved in analyzing orbits and physical objects, QML performs better than traditional machine learning in modeling the relationship between data. This method involves data-intensive preprocessing steps, such as feature selection by removing unnecessary features and correlation analysis to find predictors. Quantum circuits are used for specification and classification, and the standard evaluation is based on accuracy, recall, F1 score, and precision. A strong and weak method is provided by cross-validation and hyperparameter tuning. The best classical model here is the decision tree, which is a good model for high-resolution and low-budget social benefit with 0.883 accuracy, 0.955 recovery rate, 0.981 F1 score, and 0.883 sensitivity. However, the quantum model has made great leaps. AMSGRAD QCNN (Adaptive Moment Estimation with Gradient Thresholding Quantum Convolutional Neural Networks) achieves a non-uniform accuracy of 0.997, which is 13% higher than the decision tree with 0.984 accuracy, 0.955 recovery rate, and 0.981 F1 score. The accuracy of SPSA QCNN(Simultaneous Perturbation Stochastic Approximation) is 0.993, the recall is 0.974, and the F1 score is 0.977. This improvement shows the excellent ability of the quantum model to better resolve correlation data and, more importantly, to reduce false negatives in detecting stars. These results demonstrate the ability of quantum computing to analyze complex data and provide the best results. Our future work will focus on identifying and repairing quantum circuits, as well as exploring hybrid quantum classical models to improve model accuracy and interpretation. The findings open the door to new ways to predict the simplest solutions and the most powerful and accurate way to date of estimating the danger zone.
Quantum computing (QC) has emerged as a revolutionary technology that can solve computationally rigid problems significantly faster than classical computers, potentially facilitating the development of Machine Learning (ML). This paper provides a deeper review of Quantum Machine Learning (QML), covering basic principles, quantum-classical techniques and real-world healthcare applications. The review paper evaluates the latest developments in quantum-enhanced ML models, including quantum support vector (QSVMs), quantum neural networks (QNNs), quantum principal component (QPCAs), quantum convolutional neural networks (QCNNs) and quantum K-nearest neighbor (QKNNs) techniques. Additionally, the review paper discusses complex challenges such as low computational power, huge, overfitting risk, high-dimensional and noisy datasets in classical ML techniques. Finally, the review paper summarizes the effectiveness of QML techniques compared with classical ML classification techniques based on their accuracy values. This review serves as a comprehensive resource for researchers exploring the classification of various non-communicable diseases (NCDs) on the intersection of quantum computing and machine learning.
Quantum machine learning (QML) combined with classical machine learning provides hitherto unexplored computational capabilities for problem resolution. The goal of this chapter is to present a thorough analysis of the ideas and uses of quantum computing capacity on machine learning methods. A full analysis of popular QML algorithms, such as quantum principal component analysis, quantum support vector machines, and quantum neural networks, is conducted after an explanation of the foundations of the quantum concept and its distinguishing features. The critical assessment considers robustness, interpretability, and scalability in addition to evaluating the benefits and drawbacks of each method. The primary focus is on the merging of classical and quantum models, looking at areas of convergence that make use of the most advantageous aspects of both paradigms. This work addresses challenges associated with putting quantum algorithms into practice, including hardware constraints and error-reduction strategies. This chapter evaluates the state of quantum computing technologies and their applicability to real-world quantum machine learning applications, in addition to reviewing particular algorithms. This is jam-packed with cutting-edge discoveries and trends that shed light on where QML research will go in the future. The final section of this chapter discusses potential future paths for QML, emphasizing the need for unified benchmarks, enhanced quantum hardware, and cooperative efforts between the machine learning and quantum computing groups. This concise review attempts to serve as a useful reference for scholars, professionals, and hobbyists navigating the quickly evolving field of quantum-enhanced machine learning by providing an extensive overview of QML algorithms.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.