量子三态神经网络在金融预测中表现更优，训练更快且更稳定。
Quantum inspired qubit qutrit neural networks for real time financial forecasting
- 用量子三态构造神经网络，提升模型表达能力。
- 三态模型预测准确率超70%，夏普比率与信息系数均最优。
- 适合对实时性要求高的金融场景，如高频交易系统。
本研究对比了人工神经网络（ANN）、量子比特神经网络（QQBN）和量子三态神经网络（QQTN）在股票预测中的表现。尽管所有模型准确率均超过70%，但量子三态神经网络在风险调整收益（夏普比率）、预测一致性（信息系数）及不同市场条件下的鲁棒性方面均显著优于其他模型。此外，该模型在保持高性能的同时，训练时间明显缩短，展现出在实时金融预测中的巨大潜力。结果表明，量子启发的三态神经网络具有高效、稳定、适应性强的优势，为计算密集型领域提供了新的技术路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。