Financial markets, particularly equity indices like the S&P 500, exhibit complex, nonlinear, and frequently chaotic behaviors driven by a combination of macroeconomic, geopolitical, and psychological factors. Traditional machine learning techniques, despite their notable success in modeling market trends, often encounter performance limitations when dealing with high-dimensional feature spaces and intricate historical dependencies. Quantum computing offers a promising approach for addressing these challenges through its ability to represent and process information in superposition states. This study proposes and implements a quantum neural network (QNN) architecture that utilizes angle encoding to embed carefully engineered features, including volatility measures, momentum indicators, and lagged price signals, into quantum states. These encoded features are processed through a customized quantum circuit comprising three variational layers that combine linear entanglement patterns with single-qubit rotation operations. The training process is conducted via gradient-based optimization through the PennyLane framework, operating within a noiseless simulation environment to establish theoretical performance bounds. The experimental results demonstrate that the QNN achieves an accuracy of 81.3% on the held-out test set in simulation, outperforming traditional baseline models including Long Short-Term Memory networks, Random Forest classifiers, and Support Vector Machines under identical conditions. The model exhibits balanced precision (0.83) and recall (0.79). While the experiments were conducted in simulation due to current quantum hardware limitations, the findings illustrate the potential of quantum-enhanced algorithms for financial forecasting applications.

Keywords

Quantum Neural Networks, Financial Forecasting, S&P 500, Variational Quantum Circuits, Time Series Prediction.