Run the numbers faster than the market moves.

Pricing, risk, and optimization all lean on Monte Carlo runs that eat CPU hours and still lag the market. Quantum amplitude estimation can reach the same answers with a fraction of the samples. Xanadu built and tested the approach with BMO and Scotiabank.

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The Challenge

Derivative pricing is a sampling problem you can't out-compute.

Pricing a portfolio of derivatives means running millions of market scenarios, every day, and faster when markets move. Conventional Monte Carlo burns millions of CPU hours and still trails real-time risk.

Quantum amplitude estimation delivers a quadratic speedup: hundreds to thousands of times fewer samples for the same confidence. We built the software suite that shows it.

How Xanadu Helps

Where quantum changes the economics of pricing and risk

Faster derivatives pricing
Banks reprice large derivative portfolios every day, and conventional Monte Carlo is slow and power-hungry.
With BMO and Scotiabank, Xanadu ran quantum Monte Carlo across multiple trading products and projected speedups of hundreds to thousands of times.
Risk and scenario analysis
Stress tests and scenario sweeps are gated by sampling cost, so risk updates lag volatile markets.
The same quadratic speedup cuts the samples needed for a given confidence, keeping risk current instead of overnight.
Portfolio optimization
High-dimensional allocation and optimization problems strain the solvers you run today.
Explore optimization problems on quantum hardware through the same PennyLane workflows your quants already prototype in.