End-to-End PDE-Based Quantum Algorithms for Multi-Asset Option Pricing under Local and Stochastic Volatility

2026-05-26

AI Summary

This work addresses the challenge of pricing high-dimensional multi-asset European options under local volatility and Heston stochastic volatility models, which entails solving intractable high-dimensional parabolic partial differential equations (PDEs). The paper introduces the first end-to-end quantum PDE framework that takes classical contract specifications and model parameters as input, discretizes the PDE via finite differences, and computes option prices using a quantum linear system solver implemented on a quantum circuit, ultimately yielding classical valuation outputs. The proposed method achieves polynomial speedups of $N^{d/2}$ and $N^d$ over classical finite difference methods under the local volatility and Heston models, respectively. The quantum algorithm is constructed from CNOT and single-qubit Pauli rotation gates, compiled into the Clifford+T gate set, with rigorous theoretical bounds on gate complexity and numerical experiments accurately reproducing option prices and implied volatility surfaces under the Heston model.

Abstract

Multi-asset option pricing under local- and stochastic-volatility models leads naturally to high-dimensional parabolic PDEs. We develop an end-to-end quantum PDE framework for European option pricing under local-volatility Black--Scholes and Heston models. The framework takes classical contract and model data as input and returns classical estimates of selected option values. We solve the pricing PDEs after finite-difference discretization on spatial grids. For $N=2^n$ grid points per spatial direction and $d$ assets, the end-to-end gate complexity for single-point recovery, counted in elementary CNOT gates and one-qubit Pauli-axis rotations, has leading grid-size dependence $\widetilde{O}(d^2 N^{2+d/2})$ for local-volatility Black--Scholes and $\widetilde{O}(d^2 N^{d+2})$ for Heston. Relative to grid-based finite-difference baselines, these scalings correspond to polynomial improvement factors $N^{d/2}$ and $N^d$, respectively. These estimates translate to Clifford+T resources via standard compilation. We complement the complexity analysis with numerical benchmarks against standard classical methods. In the Heston setting, the framework recovers option prices across strikes together with the associated implied-volatility smile/skew. Overall, this work provides a complete end-to-end quantum pricing pipeline with explicit resource accounting and theoretical performance guarantees.

Problem

• multi-asset option pricing
• local volatility
• stochastic volatility
• high-dimensional PDEs
• quantum algorithms

Innovation

• quantum PDE solver
• multi-asset option pricing
• stochastic volatility
• end-to-end quantum algorithm
• finite-difference discretization