Patent Pending: QRS-001-PROV
Quantum-native HPC architecture and heterogeneous backend scheduling.
Active Models
North Atlantic Hurricane
California Wildfire
European Wind
Japan Typhoon
The Crisis
Not because the risk was too high—because their models couldn’t price it.
Insured Losses
Billions from a single event that
exposed systemic model failure
from the LA wildfires alone.
Excess Capital Trapped
Across the reinsurance industry due
to model uncertainty margins from
outdated analytics.
FAIR Plan Surge
Legacy Monte Carlo models waste>96% of compute cycles on noise.The result: carriers fleeing entire
markets.
The Noise Tax Is Costing the Industry Billions. Legacy Monte Carlo models waste >96% of compute
cycles on noise. The result: excess capital reserves, mispriced risk, and carriers fleeing entire
markets.
How It Works
QRS replaces brute-force Monte Carlo with a quantum-native HPC architecture and
heterogeneous backend scheduler that delivers better accuracy with 96% fewer
samples.
The QRS Pipeline
North Atlantic Hurricane
Heterogeneous Scheduling
GPU Clusters · 96% Efficient
EP · PML · TVaR · 14.2s
Legacy models waste >96% of samples on noise. Our heterogeneous backend scheduler routes compute intelligently for absolute tail convergence.
The QRS engine intelligently routes compute jobs based on precision and cost, utilizing directed importance sampling across massive GPU clusters to achieve absolute tail convergence. The architecture is quantum-native: as fault-tolerant QPU hardware matures, the scheduler absorbs it as a performance upgrade—not a redesign.
By isolating the true tail amplitude, our directed importance sampling corrects classical overestimation and reduces variance by 35% at the 99.5th percentile.
From data ingestion to capital optimization in under 15 seconds. EP curves, PML, TVaR—actionable output, not raw simulation.
Validation
Reduction in Tail Variance vs. Legacy Standard
Isolating the 1-in-250 year return period without the error bars that trap surplus capital.
Peer Reviewed Research
The mathematical foundations of the QRS architecture are detailed in our published technical paper: “Hybrid Quantum-Classical Framework for Catastrophe Risk Modeling.” The paper establishes the theoretical basis for our directed importance
sampling and has been independently validated.
Automatically normalize messy CSVs to OED standards while enriching geocodes with instant soil, elevation, and roof age data.
Move from batch to real-time. Sub-500ms single-risk pricing and continuous exposure tracking.
Project exposures 5, 10, or 50 years out with SSP-conditioned catalogs and novel peril tracking.
Track every parameter change, isolate epistemic uncertainty, and generate instant Solvency II cryptographic ledgers.
Tail risk quantification at regulatory thresholds.
Industry standard catastrophe modeling benchmark.
Tested against legacy outputs.
Enterprise Risk Platform
See how QRS can quantify capital release for your book and convert
trapped reserves into competitive advantage.
