hybrid-quantum-financial-security

name	hybrid-quantum-financial-security
description	End-to-end hybrid quantum-classical financial security pipeline integrating VQC forecasting, QUBO annealing, and post-quantum cryptographic signing. Unifies prediction and optimization for financial risk systems under real market constraints. Use when: hybrid quantum finance, VQC forecasting, QUBO portfolio optimization, post-quantum cryptography in finance, end-to-end quantum financial pipelines, financial risk management.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2602.16976","published":"2026-02-13","authors":"Srikumar Nayak","tags":["quantum-finance","vqc","qubo","post-quantum","pipeline"]}

HQFS: Hybrid Quantum Classical Financial Security

Description

HQFS integrates Variational Quantum Circuit (VQC) forecasting with QUBO (Quadratic Unconstrained Binary Optimization) annealing and post-quantum cryptographic signing into a unified end-to-end pipeline for financial risk management. Addresses the fundamental gap between prediction quality and decision stability under real market constraints (lot sizes, position caps, regulatory requirements).

Core Problem

Traditional financial risk systems operate as a two-step pipeline:

1. Prediction: ML model forecasts returns/volatility
2. Optimization: Separate optimizer allocates portfolio

This split fails under real-world constraints because prediction errors compound during optimization. HQFS unifies both steps into a single trainable pipeline with audit-ready cryptographic signing.

Methodology

Step 1: VQC Forecasting

• Variational Quantum Circuit processes time-series features
• Quantum advantage via Hilbert space feature mapping
• Captures nonlinear market patterns missed by classical models
• Output: predicted returns and risk metrics

Step 2: QUBO Annealing

• Portfolio allocation formulated as QUBO problem
• Real market constraints encoded as penalty terms:
  • Lot size constraints (integer positions)
  • Position caps (maximum allocation per asset)
  • Sector exposure limits
  • Transaction cost modeling
• Solved via quantum annealing (D-Wave) or simulated annealing

Step 3: Post-Quantum Signing

• All pipeline outputs cryptographically signed
• Uses NIST-standardized post-quantum algorithms (ML-DSA, ML-KEM)
• Ensures audit readiness and tamper-proof records
• Critical for regulatory compliance in financial institutions

Key Advantages

• Unified pipeline: Prediction errors directly inform optimization
• Constraint-aware: Real market limits built into optimization
• Audit-ready: Post-quantum signatures for compliance
• End-to-end: Single pipeline replaces fragmented toolchain

Usage Patterns

Pattern 1: Financial Forecasting

Use VQC component for time-series prediction of stock returns, volatility, or trading volumes.

Pattern 2: Portfolio Optimization

Use QUBO formulation for constrained portfolio allocation under real market rules.

Pattern 3: End-to-End Pipeline

Run full HQFS pipeline: forecast -> optimize -> sign for auditable financial decisions.

Related Skills

• quantum-finance - General quantum finance patterns
• quantum-portfolio-optimization - QAOA-based portfolio optimization
• quantum-finance-pipeline - Quantum financial pipeline patterns
• qubo-federated-learning-security - QUBO in federated learning security