Quantum Monte Carlo algorithm for option pricing and its complexity analysis
For quantitative finance, this work provides a quantum algorithm with rigorous complexity guarantees for option pricing, though it is incremental as it applies known quantum Monte Carlo techniques to a specific financial problem.
The paper presents a quantum Monte Carlo algorithm for pricing options under multidimensional Black-Scholes PDEs with correlation, achieving computational complexity bounded polynomially in dimension and reciprocal of accuracy, with a proven speed-up over classical Monte Carlo for bounded payoffs.
In this paper we provide a quantum Monte Carlo algorithm to solve multidimensional Black-Scholes PDEs with correlation for option pricing. The payoff function of the option is of general form and is only required to be continuous and piecewise affine, which covers most of the relevant payoff functions used in finance. We provide a rigorous error analysis and complexity analysis of our algorithm. In particular, we prove that the computational complexity of our algorithm is bounded polynomially in the space dimension $d$ of the PDE and the reciprocal of the prescribed accuracy $\varepsilon$. Moreover, we show that for payoff functions which are bounded, our algorithm indeed has a speed-up compared to classical Monte Carlo methods. Furthermore, we provide numerical simulations in two dimensions using our developed package within the Qiskit framework tailored to price continuous piecewise affine options with respect to the Black-Scholes model, as well as discuss the potential extension of the numerical simulations to arbitrary space dimension.