English(EN) A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines

混合量子-经典模型提升金融波动性预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-07 04:00

研究人员开发了一个用于金融波动性预测的混合量子-经典框架，将长短期记忆 (LSTM) 网络与量子电路生机模型 (QCBM) 相结合。LSTM 提取时间特征，而 QCBM 对复杂的市场分布进行建模。与经典的 LSTM 基线相比，该方法在中国股市数据上显示出更高的预测准确性。 AI

影响引入了一种新颖的金融预测混合模型，通过利用量子计算进行复杂分布建模，有可能提高预测准确性。

排序理由这是一篇详细介绍用于金融预测的新颖混合量子-经典模型的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Yixiong Chen · 2026-05-07 04:00

基于量子电路出生机的混合量子-经典金融波动性预测框架

arXiv:2603.09789v2 Announce Type: replace Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional…