Study reports 9.4% Sharpe ratio improvement, 11.7% reduction in max drawdown, and 28% faster solve time versus mixed‑integer baseline.
Paper submitted to arXiv under categories cs.AI, cs.CL, cs.LG, with DOI pending registration.
📖 Full Retelling
Srikumar Nayak, an AI researcher, introduced the HQFS framework in a February 19, 2026 arXiv submission (2602.16976) that blends variational quantum circuits, quantum annealing, and post‑quantum signatures to streamline financial risk modeling, optimization, and auditability. The paper proposes a unified hybrid pipeline—“Hybrid Quantum Classical Financial Security” (HQFS)—which first predicts next‑step returns and volatility via a small classical head on a VQC, then translates the risk‑return objective into a QUBO solvable by quantum annealers (or classic solvers), and finally signs each portfolio rebalancing decision with a post‑quantum signature to provide a tamper‑proof audit trail. Empirical results on market datasets show improved forecasting accuracy (7.8% lower return prediction error, 6.1% lower volatility error), better out‑of‑sample Sharpe ratios (9.4% gain), nearly 12% lower maximum drawdown, and a 28% speedup in solve time compared to classic integer programming, all while generating signed, traceable allocation records.
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Deep Analysis
Why It Matters
HQFS combines quantum forecasting, quantum annealing, and post-quantum signatures to deliver more accurate risk predictions, faster optimization, and verifiable audit trails, addressing key pain points in modern financial risk systems.
What Happens Next
Financial institutions may adopt HQFS to enhance portfolio management and meet regulatory demands, while further research will explore scaling to larger asset sets and broader deployment scenarios.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.16976 [Submitted on 19 Feb 2026] Title: HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing Authors: Srikumar Nayak View a PDF of the paper titled HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing, by Srikumar Nayak View PDF HTML 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 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% c...
