Recent Advances in An Optimized Equivariant Split Attention Quantum Neural Network Based Recommendation System for Stock Market Prediction: A Systematic Review

Abstract

Financial markets are complex, dynamic systems characterized by nonlinear patterns, stochastic behavior, and high-dimensional data, making accurate stock market prediction a challenging task. Traditional statistical models often fail to capture these complexities, leading to the growing adoption of artificial intelligence and quantum computing techniques. This review presents a comprehensive analysis of optimized equivariant split attention quantum neural network frameworks for stock market prediction and recommendation systems. The approach integrates quantum neural networks with equivariant architectures to preserve structural relationships in financial data, while split attention mechanisms enhance the modeling of both local and global temporal dependencies. The study examines various optimization strategies, including variational quantum circuits, evolutionary algorithms, and hybrid quantum-classical training methods, focusing on their scalability and robustness. It also explores the integration of heterogeneous data sources such as price data, macroeconomic indicators, and sentiment analysis to improve prediction accuracy. Evaluations across diverse financial datasets demonstrate improved forecasting performance, generalization, and computational efficiency compared to conventional models. Overall, the framework offers a promising direction for developing intelligent, scalable, and high-performance financial prediction and recommendation systems.