Quantum computing for multidimensional option pricing: End-to-end pipeline

TL;DR

This paper presents a quantum computing framework for multi-asset option pricing, combining risk-neutral density recovery with quantum-accelerated numerical integration to improve computational efficiency and accuracy.

Contribution

It introduces an end-to-end quantum-enhanced pipeline for multidimensional option pricing, integrating arbitrage-free density calibration with quantum Monte Carlo methods.

Findings

QAMC achieves 10-100x fewer queries than classical methods.

High calibration accuracy on real market data.

Quadratic convergence of quantum algorithms improves efficiency.

Abstract

This work introduces an end-to-end framework for multi-asset option pricing that combines market-consistent risk-neutral density recovery with quantum-accelerated numerical integration. We first calibrate arbitrage-free marginal distributions from European option quotes using the Normal Inverse Gaussian (NIG) model, leveraging its analytical tractability and ability to capture skewness and fat tails. Marginals are coupled via a Gaussian copula to construct joint distributions. To address the computational bottleneck of the high-dimensional integration required to solve the option pricing formula, we employ Quantum Accelerated Monte Carlo (QAMC) techniques based on Quantum Amplitude Estimation (QAE), achieving quadratic convergence improvements over classical Monte Carlo (CMC) methods. Theoretical results establish accuracy bounds and query complexity for both marginal density estimation (via cosine-series expansions) and multidimensional pricing. Empirical tests on liquid equity entities (Credit Agricole, AXA, Michelin) confirm high calibration accuracy and demonstrate that QAMC requires 10-100 times fewer queries than classical methods for comparable precision. This study provides a practical route to integrate arbitrage-aware modelling with quantum computing, highlighting implications for scalability and future extensions to complex derivatives.