Project Lead: Oxford Quantum Circuits
Sector: Financial
Project Summary
This project investigates whether Quantum Monte Carlo (QMC) can provide a practical advantage for Value-at-Risk (VaR) calculations used in financial risk management.
Current VaR calculations rely on classical Monte Carlo simulations, which converge slowly and require significant computing resources. Existing quantum approaches are limited by unrealistic assumptions about probability distributions and by quantum circuits that are too deep for early fault-tolerant hardware.
The project will develop a distribution-agnostic QMC approach that generates Geometric Brownian Motion (GBM) directly within quantum circuits and uses Parallel Amplitude Estimation to reduce circuit depth. Rather than running on a specific quantum computer, the work uses Microsoft’s Azure Quantum Resource Estimator together with OQC hardware noise models to estimate the logical qubits, T-gates, and error-correction resources required for deployment.
Expected outcomes include a definitive resource benchmark, identification of hardware depth-versus-width trade-offs, and an architectural roadmap showing the quantum hardware requirements needed to achieve practical quantum advantage for financial risk calculations.
