AssurQuant.
Quantitative and quantum engineering for the numbers insurers bet the company on.
Two engines for insurance risk. One prices what a company should pay for its cover. The other runs the Solvency II capital calculation in full, twenty four billion valuations, and prices it on a real quantum machine. The quantitative engineering we take on for clients.
The largest routine calculation in insurance, run in full and priced on real quantum hardware.
Solvency II in full · quantum amplitude estimation · real hardware
Two engines for the numbers insurers bet the company on.
AssurQuant prices insurance risk at both ends. One engine tells a company what its group health cover should cost and where it is bleeding money. The other runs the Solvency II capital calculation, the largest routine computation in finance, in full, and prices the same risk on a real quantum machine.
Prices a company's group health cover and pinpoints where it overpays the market.
Runs the Solvency II capital calculation in full, twenty four billion valuations, not the approximation the industry settles for.
Prices the same risk with quantum amplitude estimation on a real quantum machine, not a simulator.
Three engines, one risk, priced end to end.
A pricing model for what a company pays, a Monte Carlo engine for what an insurer must hold, and a quantum kernel that prices the same risk on hardware the rest of the field only writes about.
What a company should pay, against the market
It places a company's group health cover against the whole market and returns a number with a verdict: overpaying, in line, or under. Where the real premium is on record, it shows that figure outright.
The whole Solvency II calculation, in full
The capital an insurer must hold comes from nested Monte Carlo: thousands of scenarios, each demanding thousands of revaluations, twenty four billion computations for one number. Most of the industry runs an approximation. This runs it exactly, on a closed form validated model, and benchmarks the fast path against the exact answer.
Amplitude estimation on a real machine
The same capital and risk measures, expected loss, VaR, CVaR and the tail, priced by iterative quantum amplitude estimation on a real quantum computer with error suppression. Not a simulation of quantum. Quantum.
The size of the calculation, the money on it, and the machine.
Getting the capital number slightly wrong is not an academic problem. It is a nine figure one, every year, across the market.
What a single point of Solvency II capital error costs the French life market annually.
Every scenario in the nested capital calculation, computed in full rather than approximated.
How close the quantum amplitude estimation lands to the exact value, on a real quantum machine.
Built for the machine that is coming.
Both engines run today, the classical one exact, the quantum one on real hardware. The frontier is the crossover, the point where the quantum kernel overtakes the classical run, and AssurQuant is built to cross it the day the hardware allows.
Both engines, live
Pricing and capital both run today, the capital number computed classically and on a real quantum machine.
The crossover
Pinning the exact point where quantum amplitude estimation overtakes the classical run on real hardware.
Peer reviewed
The paper is written, with reproducible figures. Submission to actuarial and quantitative venues is next.
Quant and quantum engineering for the numbers you cannot get wrong.
Heavy quantitative and quantum computing, aimed at risk that regulators and boards act on. When your numbers have to be right, this is who builds them.
Quantum computing
Real algorithms on real quantum hardware, error suppression, and a hard read on exactly where quantum overtakes classical.
Large scale simulation
Nested Monte Carlo at tens of billions of valuations, validated stochastic models, variance reduction, and GPU acceleration built for it.
Data engineering at scale
Tens of thousands of documents turned into clean, structured training data by pipelines that resume, dedupe and never lose a field.
Applied ML that holds up
Models with calibrated intervals, measured out of sample, that have to beat a real benchmark before they ship.
Regulated domain modelling
Solvency II, actuarial calibration, and the figures insurers answer to a regulator for, built to a standard you can put in front of one.
Reproducible from clone
Pipelines with test and regression gates, and results anyone can rebuild from a clean checkout.
Questions
Two quantitative engines for insurance: one prices what a company should pay for its group health cover, the other runs the Solvency II capital calculation in full and prices the same risk on a real quantum machine. It is a La Boetie lab build, and it shows the quant and quantum engineering we do for clients.
For the numbers you cannot get wrong.
Quantitative modelling, quantum computing, simulation at scale, wherever a wrong figure is expensive. Tell us the number you need to trust, and we will build the engine that earns it.