Abstract
Accurate forecasting of financial market volatility is crucial for risk management, option pricing, and portfolio optimization. Traditional econometric models and classical machine learning methods face challenges in handling the inherent non-linear and non-stationary characteristics of financial time series. In recent years, the rapid development of quantum computing has provided a new paradigm for solving complex optimization and sampling problems. This paper proposes a novel hybrid quantum-classical computing framework aimed at combining the powerful representation capabilities of classical neural networks with the unique advantages of quantum models. For the specific task of financial market volatility forecasting, we designed and implemented a hybrid model based on this framework, which combines a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM is responsible for extracting complex dynamic features from historical time series data, while the QCBM serves as a learnable prior module, providing the model with a high-quality prior distribution to guide the forecasting process. We evaluated the model on two real financial datasets consisting of 5-minute high-frequency data from the Shanghai Stock Exchange (SSE) Composite Index and CSI 300 Index. Experimental results show that, compared to a purely classical LSTM baseline model, our hybrid quantum-classical model demonstrates significant advantages across multiple key metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and QLIKE loss, proving the great potential of quantum computing in enhancing the capabilities of financial forecasting models. More broadly, the proposed hybrid framework offers a flexible architecture that may be adapted to other machine learning tasks involving high-dimensional, complex, or non-linear data distributions.
Abstract (translated)
金融市场波动的准确预测对于风险管理、期权定价和投资组合优化至关重要。传统的计量经济学模型和经典机器学习方法在处理金融时间序列内在的非线性和非平稳特性时面临挑战。近年来，量子计算技术的迅速发展为解决复杂的优化和采样问题提供了一个新的范式。本文提出了一种新颖的混合量子-经典计算框架，旨在结合经典神经网络的强大表示能力与量子模型的独特优势。针对金融市场波动预测这一特定任务，我们基于该框架设计并实现了一个融合长短期记忆（LSTM）网络与量子电路诞生机（QCBM）的混合模型。LSTM负责从历史时间序列数据中提取复杂的动态特征，而QCBM作为可学习先验模块，为模型提供高质量的先验分布以指导预测过程。 我们在由上海证券交易所综合指数和沪深300指数组成的两个真实金融数据集上对该模型进行了评估。这些数据集包含5分钟高频数据。实验结果表明，与仅基于经典LSTM网络的基准模型相比，我们的混合量子-经典模型在均方误差（MSE）、均方根误差（RMSE）和QLIKE损失等多个关键指标上展现出显著优势，证明了量子计算技术在增强金融预测模型能力方面的巨大潜力。更广泛地说，所提出的混合框架提供了一种灵活的架构，可以适应其他涉及高维、复杂或非线性数据分布的机器学习任务。 该研究不仅验证了混合量子-经典模型在金融市场波动预测中的优越性能，还为未来将量子计算技术应用于更多领域的探索铺平道路。
URL
https://arxiv.org/abs/2603.09789
