A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines

TL;DR

This paper introduces a hybrid quantum-classical model combining LSTM and Quantum Circuit Born Machines to improve financial volatility forecasting, demonstrating superior accuracy on high-frequency market data.

Contribution

It presents a novel hybrid framework integrating classical neural networks with quantum generative models, enabling quantum-enhanced forecasting without quantum inference during deployment.

Findings

The hybrid model outperforms classical LSTM in MSE, RMSE, and QLIKE metrics.

The stochastic Drop-Prior mechanism improves the model's ability to distill structured information.

The approach enables quantum-assisted training with classical inference, reducing quantum latency.

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 sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic ``Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of ``quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.