A Comparative Technique with Credit card based Fraud detection with Machine Learning and Quantum Algorithm

Quantum computing greatly benefit a wide range of fields, including the detection of credit card fraud, through its twin characteristics of improved computational power and efficiency. Quantum algorithms are used to enhance data analysis, pattern recognition and optimization properly with increased speed and efficiency. In the proposed model, we investigate how quantum machine learnings algorithms (QML) are used to spot credit card fraud using a data set that includes transactions from European cardholders. Various models such as neural network activation functions, epochs, and other factors employed in a hybrid quantum-classical method. The results from the best model employing a quantum hybrid technique, a conventional neural network, and an ML approach are compared. For equivalent prediction performance, we find that the quantum models train substantially more effectively than the conventional models. In order to implement Quantum algorithms, penny lane is used for enabling differentiable scripting of quantum computers that is cross-platform. Similar to how a neural network is trained, a quantum computer is trained. Overall, this in-depth investigation of the application of hybrid QML in credit card fraud detection shows significant promise.