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Quantum Machine Intelligence

Sarcopenia risk prediction and feature selection by using quantum machine learning algorithms

Ubaid Ullah, Danyal Maheshwari, Cristián Castillo-Olea, Begonya García-Zapirain

Published Nov 16, 2024• 3 citations
Publisher
DOI
10.1007/s42484-024-00218-4

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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.
The synergy between quantum computing and machine learning gives future potential in the financial sector. The unique features such as superposition and entanglement in quantum computing are utilized in Quantum Machine Learning (QML) to solve the challenges that dominate the modern era of finance. The subject of this paper explains the use of Quantum Machine Learning (QML) in finance including risk management, portfolio optimization, market trend forecasting, and fraud detection. In portfolio optimization, the aim is to reduce risk at the same time maximizing return, the quantum-enhanced methodology does it better than the classical method. QML is applied for risk analysis because it efficiently computes values such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In finance, improved sentiment analysis, anomaly detection to prevent fraud, and prediction of market trends are enabled with QML algorithms. QML improves credit rating, financial time series forecasting, and derivative pricing with its ability to process large amounts of data.
Abstract Non-performing loans present a significant challenge to financial institutions, driven by the complexity of the dataset, default probability, and default correlation(Bellotti et al., 2019). To mitigate this risk, this study investigates the potential of Machine Learning (ML) and Quantum Machine Learning (QML) algorithms for forecasting loan risk. Using a dataset from Kaggle, we conducted a comparative analysis between Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM).Our result using a dataset of 12,368 records and 12 features shows that the QSVM model outperformed SVM, with a higher true positive rate (93.2.%) and true negative rate (87.6%), demonstrating better performance in identifying both default and non-default cases. Additionally, QSVM exhibits a lower false negative rate indicating its superior ability to minimize clients likely to default. The AUC score of 1.0 for the QSVM further demonstrates its exceptional ability in loan prediction. While the dataset used allowed for a solid comparison, QSVM demonstrated its capacity to continue improving with larger datasets, showing its scalability and strong potential application in loan risk forecasting especially with larger datasets.