Quantum inspired qubit qutrit neural networks for real time financial forecasting

TL;DR

This study compares classical, qubit-based, and qutrit-based quantum neural networks for stock prediction, demonstrating that qutrit models outperform others in accuracy, robustness, and training efficiency for real-time financial forecasting.

Contribution

It introduces and evaluates quantum qutrit neural networks, showing their advantages over classical and qubit-based models in financial prediction tasks.

Findings

Qutrit neural networks outperform qubit and classical models in accuracy and robustness.

Qutrit models achieve comparable performance with significantly reduced training times.

All models maintain over 70% accuracy in stock prediction.

Abstract

This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outlining methodologies, architectures, and training procedures, the study highlights significant differences in training times and performance metrics across models. While all models demonstrate robust accuracies above 70%, the Quantum Qutrit-based Neural Network consistently outperforms with advantages in risk-adjusted returns, measured by the Sharpe ratio, greater consistency in prediction quality through the Information Coefficient, and enhanced robustness under varying market conditions. The QQTN not only surpasses its classical and qubit-based counterparts in multiple quantitative and qualitative metrics but also achieves comparable performance with significantly reduced training times. These results showcase the promising prospects of Quantum Qutrit-based Neural Networks in practical financial applications, where real-time processing is critical. By achieving superior accuracy, efficiency, and adaptability, the proposed models underscore the transformative potential of quantum-inspired approaches, paving the way for their integration into computationally intensive fields.