A Survey of Methods and Architectures for An Optimized Equivariant Split Attention Quantum Neural Network Based Recommendation System for Stock Market Prediction

Abstract

The financial markets of the twenty-first century present highly complex and dynamic environments that challenge traditional computational models for stock market prediction. Classical approaches such as autoregressive models, support vector regression, and conventional neural networks often fail to capture nonlinear dependencies, long-range temporal patterns, and high-dimensional interactions inherent in financial data. This limitation has motivated the exploration of quantum computing as a novel paradigm capable of leveraging superposition and entanglement to represent complex feature spaces efficiently. This survey investigates the integration of quantum neural networks with equivariant deep learning and split attention mechanisms for enhanced financial forecasting. Equivariance enables models to preserve structural relationships under transformations, improving robustness across varying market conditions, while split attention facilitates efficient handling of heterogeneous financial features such as price, volume, sentiment, and macroeconomic indicators. Furthermore, the study situates these architectures within an intelligent recommendation framework that extends beyond prediction to portfolio optimization and decision support. The survey reviews optimization techniques, datasets, and evaluation metrics across global financial markets, highlighting both performance gains and practical limitations. Key challenges identified include hardware noise, lack of real-world deployment validation, and limited interpretability. This work provides a structured foundation for advancing quantum-enhanced financial intelligence systems.