Hybrid Quantum‑Machine Learning Combats Stock Market Trends
In a groundbreaking advancement at the intersection of quantum computing and artificial intelligence , researchers have introduced a novel hybrid quantum deep learning model—BLS-QLSTM (Broad Learning System–Quantum Long Short-Term Memory)—designed to forecast stock market indices with high precision. This hybrid model harnesses the pattern recognition strength of deep learning and the superior computational parallelism of quantum algorithms to analyze complex, non-linear financial time series data. Traditional machine learning models often struggle with market volatility, high-dimensional data, and chaotic fluctuations. However, BLS-QLSTM integrates quantum-inspired gates into the LSTM architecture to capture long-term dependencies while reducing computational overhead. The inclusion of Broad Learning allows for fast feature mapping and layered adaptability, making the model both efficient and robust against overfitting. Empirical evaluations using real-world stock datasets—su...