Integrating Quantum Computing Algorithms with Predictive Analytics of Customer Behaviour in E-Commerce

The remarkable expansion of e-commerce, together with its competitive market, has created difficulties for traditional machine learning models to forecast customer actions when dealing with intricate datasets. The encountered problems with conventional machine learning models in predictive analytics for customer behaviour in e-commerce have led researchers to explore quantum computing algorithms as a potential solution. The main goal of this research work is to analyse how quantum computing technology and quantum algorithms can address the problems of inaccuracy and high dimensionality and speed and unbalanced datasets. The system implementation integrates quantum infrastructure through quantum processing units that utilize parallel processing to handle large datasets efficiently. The proposed algorithms for customer behaviour prediction models include Quantum Support Vector Machines, Quantum K-means Clustering, Quantum Boltzmann Machines, Quantum Principal Component Analysis, Quantum Neural Networks, and Quantum Ensemble Learning. These algorithms use quantum parallelism to achieve better accuracy while speeding up computations and handling unbalanced datasets. The AI framework combines traditional machine learning pipeline with quantum computing enhancements to implement quantum algorithms. The framework consists of data pre-processing followed by Quantum Machine Learning (QML) model training and Quantum-Powered AI to address imbalanced data while reducing prediction biases. Real-time insights and visualization and analytics from the advance predictive analytics dashboard enable stakeholders to make data-driven decisions. The system implements security and privacy measures through quantum cryptography which uses quantum cryptographic algorithms to boost customer data security. The research results present a potential solution to predictive analytics challenges which would transform the E-commerce industry.