语境量子神经网络用于股票价格预测
Sharan Mourya1,2, Hannes Leipold3, Bibhas Adhikari3
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, US. sharanmourya7@gmail.com.
Scientific reports
|January 7, 2026
概括
量子机器学习 (QML) 通过捕捉最近的趋势来预测股价分布. 这种方法通过考虑资产之间的相关性来提高财务建模的效率和准确性.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 计算金融是指计算金融.
背景情况:
- 传统的股票价格预测模型通常依赖于广泛的历史数据,可能错过了最近的市场趋势.
- 量子机器学习 (QML) 为计算问题提供了新的方法,包括财务预测.
研究的目的:
- 应用QML来预测多种资产的股价分布.
- 开发一种QML模型,比传统方法更有效地捕捉最近的趋势和资产间的相关性.
主要方法:
- 利用上下文量子神经网络进行股价分布预测.
- 引入了量子批量梯度更新 (QBGU) 以加速训练和改善融合.
- 开发了一种量子多任务学习 (QMTL) 架构,即共享和指定替代品,用于同时训练多个资产.
主要成果:
- 拟议的QML方法与量子单任务学习 (QSTL) 模型相比显示出更高的性能.
- 该模型有效地捕获了资产间的相关性,从而实现了更准确的预测.
- 对主要科技股的标普500指数数据进行实验验证实了该方法的有效性.
结论:
- 在金融应用中,QML具有显著的变革潜力,特别是在股票价格预测方面.
- 开发的QMTL架构为复杂的金融建模提供了一种资源高效的方法.
- 未来的研究可以探索先进的,资源高效的量子算法,以进行增强的财务分析.
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