量子启发的量子比特 qutrit神经网络用于实时金融预测
Kanishk Bakshi1, Kathiravan Srinivasan2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|August 6, 2025
概括
基于量子Qutrit的神经网络 (QQTNs) 与人工神经网络 (ANNs) 和基于量子Qubit的神经网络 (QQBNs) 相比,显示出优越的股票预测性能. QQTN为金融应用提供了更高的准确性,效率和稳定性.
科学领域:
- 计算金融是指计算金融.
- 量子计算是一种量子计算.
- 机器学习 机器学习
背景情况:
- 传统的机器学习模型在复杂的财务预测中面临着挑战.
- 量子计算提供了新的方法来增强计算能力.
- 评估用于股票市场预测的量子启发模型是一个新兴的研究领域.
研究的目的:
- 为了比较人工神经网络 (ANN),基于量子量子比特的神经网络 (QQBNs) 和基于量子量子比特的神经网络 (QQTNs) 的有效性,用于库存预测.
- 评估绩效指标,包括准确性,风险调整回报和稳定性.
- 评估不同量子启发和经典模型的训练效率.
主要方法:
- 实现和比较ANN,QQBNs和QQTNs架构.
- 使用历史股票市场数据进行培训和评估.
- 分析性能指标,如准确性,夏普比率和信息系数.
主要成果:
- 所有模型都实现了超过70%的预测准确度.
- 基于量子Qutrit的神经网络 (QQTNs) 在风险调整回报 (夏普比率) 和预测一致性 (信息系数) 中表现优异.
- 在不同的市场条件下,QQTN表现出更强的稳定性,并且在显著缩短的培训时间下取得了可比的结果.
结论:
- 基于量子Qutrit的神经网络为股票预测和需要实时处理的金融应用提供了有希望的进步.
- 该研究强调了量子启发方法在财务预测中超越经典和量子比特模型的潜力.
- QQTN提供了准确性,效率和适应性的引人注目的组合,为其融入计算密集型领域铺平了道路.
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