A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
arXiv:2603.09789v3 Announce Type: replace-cross 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 (Q
The increasing maturity of quantum computing research and the demand for more accurate financial forecasting methods are converging to produce novel hybrid approaches.
This development indicates quantum computing's growing potential to offer a significant edge in complex financial modeling, potentially disrupting traditional quantitative finance methodologies.
The financial industry now has a plausible new avenue for enhanced volatility forecasting, moving beyond purely classical models by integrating quantum capabilities.
- · Quantitative finance firms
- · Quantum computing companies
- · Financial institutions with large trading operations
- · Traditional pure classical financial modeling firms
Financial markets could see improved stability and efficiency due to more accurate risk assessment and pricing.
Increased investment and R&D into quantum finance solutions, accelerating the timeline for practical quantum applications in financial services.
Nations or blocs with leading quantum computing capabilities could gain a strategic advantage in global financial markets.
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Read at arXiv cs.AI