HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing
arXiv:2602.16976v1 Announce Type: new Abstract: Here's the corrected paragraph with all punctuation and formatting issues fixed: Financial risk systems usually follow a two-step routine: a model predicts return or risk, and then an optimizer makes a decision such as a portfolio rebalance. In practice, this split can break under real constraints. The prediction model may look good, but the final decision can be unstable when the market shifts, when discrete constraints are added (lot sizes, caps), or when the optimization becomes slow for larger asset sets. Also, regulated settings need a clear audit trail that links each decision to the exact model state and inputs. We present HQFS, a practical hybrid pipeline that connects forecasting, discrete risk optimization, and auditability in one flow. First, HQFS learns next-step return and a volatility proxy using a variational quantum circuit (VQC) with a small classical head. Second, HQFS converts the risk-return objective and constraint
arXiv:2602.16976v1 Announce Type: new Abstract: Here's the corrected paragraph with all punctuation and formatting issues fixed: Financial risk systems usually follow a two-step routine: a model predicts return or risk, and then an optimizer makes a decision such as a portfolio rebalance. In practice, this split can break under real constraints. The prediction model may look good, but the final decision can be unstable when the market shifts, when discrete constraints are added (lot sizes, caps), or when the optimization becomes slow for larger asset sets. Also, regulated settings need a clear audit trail that links each decision to the exact model state and inputs. We present HQFS, a practical hybrid pipeline that connects forecasting, discrete risk optimization, and auditability in one flow. First, HQFS learns next-step return and a volatility proxy using a variational quantum circuit (VQC) with a small classical head. Second, HQFS converts the risk-return objective and constraints into a QUBO and solves it with quantum annealing when available, while keeping a compatible classical QUBO solver as a fallback for deployment. Third, HQFS signs each rebalance output using a post-quantum signature so the allocation can be verified later without trusting the runtime environment. On our market dataset study, HQFS reduces return prediction error by 7.8% and volatility prediction error by 6.1% versus a tuned classical baseline. For the decision layer, HQFS improves out-of-sample Sharpe by 9.4% and lowers maximum drawdown by 11.7%. The QUBO solve stage also cuts average solve time by 28% compared to a mixed-integer baseline under the same constraints, while producing fully traceable, signed allocation records.
Executive Summary
This article presents HQFS, a novel financial risk management system that integrates forecasting, discrete risk optimization, and auditability through a hybrid quantum-classical pipeline. HQFS employs a variational quantum circuit (VQC) for return and volatility prediction, followed by a QUBO-based optimization stage using quantum annealing and a classical fallback. The system culminates in post-quantum signature-based audit-ready allocation records. Empirical results demonstrate HQFS's efficacy in reducing prediction errors, improving risk-adjusted returns, and reducing computational time. The proposed system offers a promising solution to the limitations of conventional financial risk management approaches, particularly in handling constraints and ensuring auditability.
Key Points
- ▸ HQFS combines forecasting, discrete risk optimization, and auditability in a single pipeline.
- ▸ VQC is used for return and volatility prediction, reducing prediction errors.
- ▸ QUBO-based optimization stage leverages quantum annealing and a classical fallback for efficient solutions.
- ▸ Post-quantum signature-based audit-ready allocation records ensure transparency and trustworthiness.
Merits
Improved Prediction Accuracy
HQFS's VQC-based forecasting component achieves lower prediction errors compared to classical baselines, indicating its potential for enhanced risk management.
Enhanced Optimization Efficiency
The QUBO-based optimization stage, utilizing quantum annealing and a classical fallback, demonstrates improved computational efficiency and scalability.
Audit-Ready Allocation Records
HQFS's post-quantum signature-based audit-ready allocation records ensure transparency, trustworthiness, and compliance with regulatory requirements.
Demerits
Limited Scalability
The current study's dataset and implementation may not fully capture the system's scalability and performance in larger, more complex financial environments.
Quantum Hardware Dependence
HQFS's reliance on quantum annealing and VQC may limit its deployment and maintenance, particularly in the absence of widespread access to reliable quantum hardware.
Expert Commentary
The article presents a compelling case for HQFS as a pioneering financial risk management system. While it addresses significant limitations of conventional approaches, its scalability and deployment depend on the availability and reliability of quantum hardware. Furthermore, the article's focus on auditability and transparency highlights the need for regulatory frameworks to accommodate the unique characteristics of quantum computing in finance. As the field continues to evolve, HQFS serves as a valuable reference point for the development of more sophisticated financial risk management systems.
Recommendations
- ✓ Future research should focus on scaling HQFS to larger, more complex financial environments, addressing potential challenges and limitations.
- ✓ The development of more robust quantum hardware and algorithms is crucial for the widespread adoption of systems like HQFS in finance.
