Quantum Variational Solving of Nonlinear and Multi-Dimensional Partial Differential Equations

A variational quantum algorithm for numerically solving partial differential equations (PDEs) on a quantum computer was proposed by Lubasch et al. In this paper, we generalize the method introduced by Lubasch et al. to cover a broader class of nonlinear PDEs as well as multidimensional PDEs, and study the performance of the variational quantum algorithm on several example equations. Specifically, we show via numerical simulations that the algorithm can solve instances of the Single-Asset Black-Scholes equation with a nontrivial nonlinear volatility model, the Double-Asset Black-Scholes equation, the Buckmaster equation, and the deterministic Kardar-Parisi-Zhang equation. Our simulations used up to $n=12$ ansatz qubits, computing PDE solutions with $2^n$ grid points. We also performed proof-of-concept experiments with a trapped-ion quantum processor from IonQ, showing accurate computation of two representative expectation values needed for the calculation of a single timestep of the nonlinear Black--Scholes equation. Through our classical simulations and experiments on quantum hardware, we have identified -- and we discuss -- several open challenges for using quantum variational methods to solve PDEs in a regime with a large number ($\gg 2^{20}$) of grid points, but also a practical number of gates per circuit and circuit shots.Comment: 16 pages, 10 figures (main text

Novel fitted multi-point flux approximation methods for options pricing

It is well known that pricing options in finance generally leads to the resolution of the second order Black-Scholes Partial Differential Equation (PDE). Several studies have been conducted to solve this PDE for pricing different type of financial options. However the Black-Scholes PDE has an analytical solution only for pricing European options with constant coefficients. Therefore, the resolution of the Black-Scholes PDE strongly relies on numerical methods. The finite difference method and the finite volume method are amongst the most used numerical methods for its resolution. Besides, the BlackScholes PDE is degenerated when stock price approaches zero. This degeneracy affects negatively the accuracy of the numerical method used for its resolution, and therefore special techniques are needed to tackle this drawback. In this Thesis, our goal is to build accurate numerical methods to solve the multidimensional degenerated Black-Scholes PDE. More precisely, we develop in two dimensional domain novel numerical methods called fitted Multi-Point Flux Approximation (MPFA) methods to solve the multi-dimensional Black-Scholes PDE for pricing American and European options. We investigate two types of MPFA methods, the O-method which is the classical MPFA method and the most intuitive method, and the L-method which is less intuitive, but seems to be more robust. Furthermore, we provide rigorous convergence proofs of a fully discretized schemes for the one dimensional case of the corresponding schemes, which will be well known on the name of finite volume method with Two Point Flux Approximation (TPFA) and the fitted TPFA. Numerical experiments are performed and proved that the fitted MPFA methods are more accurate than the classical finite volume method and the standard MPFA methods

Application of Operator Splitting Methods in Finance

Financial derivatives pricing aims to find the fair value of a financial contract on an underlying asset. Here we consider option pricing in the partial differential equations framework. The contemporary models lead to one-dimensional or multidimensional parabolic problems of the convection-diffusion type and generalizations thereof. An overview of various operator splitting methods is presented for the efficient numerical solution of these problems. Splitting schemes of the Alternating Direction Implicit (ADI) type are discussed for multidimensional problems, e.g. given by stochastic volatility (SV) models. For jump models Implicit-Explicit (IMEX) methods are considered which efficiently treat the nonlocal jump operator. For American options an easy-to-implement operator splitting method is described for the resulting linear complementarity problems. Numerical experiments are presented to illustrate the actual stability and convergence of the splitting schemes. Here European and American put options are considered under four asset price models: the classical Black-Scholes model, the Merton jump-diffusion model, the Heston SV model, and the Bates SV model with jumps

Pricing Financial Derivatives using Radial Basis Function generated Finite Differences with Polyharmonic Splines on Smoothly Varying Node Layouts

In this paper, we study the benefits of using polyharmonic splines and node layouts with smoothly varying density for developing robust and efficient radial basis function generated finite difference (RBF-FD) methods for pricing of financial derivatives. We present a significantly improved RBF-FD scheme and successfully apply it to two types of multidimensional partial differential equations in finance: a two-asset European call basket option under the Black--Scholes--Merton model, and a European call option under the Heston model. We also show that the performance of the improved method is equally high when it comes to pricing American options. By studying convergence, computational performance, and conditioning of the discrete systems, we show the superiority of the introduced approaches over previously used versions of the RBF-FD method in financial applications