HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing

Technical Deep Dive: HQFS

Overview

HQFS is a hybrid quantum-classical pipeline for financial risk management that aims to address several limitations in existing workflows:

1. Disconnected Risk Prediction and Optimization: Many existing pipelines treat risk prediction and portfolio optimization as separate steps, leading to challenges in linking uncertainty from the predictive model to the downstream allocation.
2. Computational Complexity of Optimization: As the number of assets, constraints, or discrete choices grows, portfolio optimization can become prohibitively expensive, pushing teams toward simplified formulations or local heuristics.
3. Lack of Auditability: Enterprise settings often require traceable outputs that link each allocation decision to the exact model version and inputs, which is often missing in standard ML-to-optimizer workflows.

Methodology

HQFS addresses these gaps through a three-part approach:

1. Hybrid Forecasting: HQFS learns joint return and volatility signals using a variational quantum circuit (VQC) with a small classical head. This provides the optimizer with consistent risk-return inputs.
2. QUBO Optimization: HQFS maps the constrained portfolio optimization problem to a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using quantum annealing hardware or a classical QUBO solver.
3. Audit-Ready Signing: HQFS adds a post-quantum signature to each rebalance output, linking the allocation to the exact model state and time window for compliance review.

Data & Experimental Setup

• The authors use the Kaggle S&P 500 stock data dataset, preprocessing the raw price series into log-returns and a volatility proxy.
• They follow a strict chronological train-validation-test split to evaluate future-time generalization.
• Hyperparameters are tuned on the validation set, and the best models are retrained on the full training set before testing.

Results

Forecasting Accuracy

• HQFS achieves the best joint accuracy in predicting both return and volatility, outperforming classical time-series models as well as deep learning baselines like LSTM, GRU, and Transformer.

Trading Performance

• In the end-to-end portfolio optimization, HQFS improves the Sharpe ratio by 9.4% and reduces maximum drawdown by 11.7% compared to a classical mean-variance approach.
• HQFS also reduces average optimization time by 28% compared to a mixed-integer baseline under the same constraints.

Audit Overhead

• The compact post-quantum signature added by HQFS incurs negligible overhead (< 5ms per rebalance) while providing a verifiable audit trail.

Interpretation

The key innovations in HQFS are:

• The hybrid VQC-based forecaster that jointly models return and volatility.
• The QUBO formulation that enables efficient discrete optimization, with a quantum annealing interface as well as a classical fallback.
• The audit-ready signing mechanism that links each allocation decision to the underlying model state.

These components work together to create a practical, deployable system that addresses the limitations of existing financial risk management pipelines.

Limitations & Uncertainties

• The authors do not explore the impact of stress regimes or market drifts, which could affect the stability of the forecasting and optimization components.
• The covariance estimation method is relatively simple, and scaling to larger asset universes may require more advanced techniques like shrinkage or factor models.
• The evaluation is limited to a single dataset, and further testing on other financial datasets would strengthen the generalizability of the results.

What Comes Next

The authors outline four directions for future work:

1. Stress testing and drift-aware retraining to maintain signal stability.
2. Strengthening covariance estimation for larger-scale portfolios.
3. Incorporating more realistic trading constraints into the QUBO formulation.
4. Evaluating additional post-quantum signature schemes and generating compact audit reports.

By addressing these areas, the HQFS pipeline can be further hardened and extended to handle real-world financial risk management challenges.