A Comprehensive Review of An Optimized Equivariant Split Attention Quantum Neural Network Based Recommendation System for Stock Market Prediction

Abstract

The rapid evolution of financial technology has driven the adoption of advanced computational techniques for stock market prediction. Traditional statistical models often fail to capture the nonlinear, stochastic, and high-dimensional nature of financial time series, leading to increased interest in hybrid approaches combining deep learning and quantum computing. This review presents an optimized equivariant split attention quantum neural network framework for stock market prediction and recommendation systems. The proposed approach integrates quantum neural networks with equivariant architectures to enhance feature representation and generalization, while split attention mechanisms improve temporal dependency modeling and computational efficiency. The study explores optimization techniques such as quantum circuit pruning, adaptive hyperparameter tuning, and hardware-aware neural architecture search. It evaluates performance using benchmark datasets from major stock exchanges, incorporating price data, macroeconomic indicators, and sentiment features. Results indicate improved prediction accuracy, reduced error, and enhanced risk-adjusted returns compared to conventional models such as LSTM and transformer architectures. Overall, the framework demonstrates strong potential for real-time trading applications, offering a scalable and efficient solution for next-generation intelligent financial forecasting systems.