QuantumEnhanced AI for Email Categorization A CNNLSTM Hybrid Approach

Email categorization is a critical task in organizing and managing email content, especially with the growing volume of digital communication. This project presents a Quantum-Enhanced AI approach for email categorization, leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The system is designed to classify emails into categories such as Primary, Social, Promotions, Updates, and Spam/Junk. To further enhance performance, the project also explores the use of traditional machine learning algorithms, including Support Vector Machine (SVM) and XGBoost, integrated with quantum computing techniques. By incorporating quantum computing, the project aims to capture complex patterns and improve classification accuracy, providing a more efficient and scalable solution for handling large-scale email datasets. The hybrid CNN-LSTM model performs feature extraction and sequential analysis, while the SVM and XGBoost classifiers offer strong performance for non-sequential data. The proposed system is implemented using Python, with libraries such as TensorFlow for CNN-LSTM, scikit-learn for SVM and XGBoost, and quantum computing frameworks for quantum-enhanced processing. This approach provides a robust, real-time solution for automated email categorization, reducing human intervention and improving productivity. By integrating quantum computing with deep learning and classical machine learning, the project aims to push the boundaries of email categorization technology and enhance the overall efficiency of email management systems.

Keywords: Email Categorization, Quantum Computing, Hybrid Model, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), XGBoost, Deep Learning, Machine Learning, Email Management, Quantum-Enhanced AI, Classification, Real-Time Processing.