"Performance Evaluation of Classical and Quantum-Inspired Pipelines for Fraud Detection Using Quantum Feature Metrics and Cloud Latency Analysis"
Keywords:
Quantum-Inspired Computing, Classical Pipeline, Quantum Feature Metric, Cloud Latency, Fraud Detection, Financial Analytics, Residual Analysis, Machine Learning, Quantum Optimization, Cloud Computing Performance.Abstract
The research introduces a comparison of classical and quantum-inspired overloaded computing techniques used for detecting fraud in the cloud. The aim of the analysis was to determine the performance, stability, and efficiency of quantum-inspired algorithms in dealing with huge financial data by combining quantum features and cloud latency parameters. The findings indicate that quantum-inspired pipelines always provide better quality of features and more sensitivity of the model compared to the classical methods, mostly when it comes to the conditions of fraudulent transactions. Besides, the scrutinization of extremely long latencies reveals that quantum-inspired methods have been more stable in processing and the cloud communication delays have been less variable. Standardized residuals' figures and Q-Q plots have validated the normality and robustness of the data distribution across both pipelines. The outcome has pointed out that there is an apparent gain of quantum-inspired systems in attaining higher quantum feature metrics with a very small sacrifice in computational latency. This study lays down a solid quantitative groundwork for the pairing of the quantum-inspired models with AI-driven anti-fraud analytics and also the development of transaction monitoring systems that are more adaptable, secure, and efficient.Downloads
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