Variational quantum simulations of stochastic differential equations

TL;DR

This paper introduces a hybrid quantum-classical algorithm using variational quantum simulation to efficiently solve stochastic differential equations by embedding probability distributions in quantum states, enabling potential quantum speed-up.

Contribution

The authors propose a novel quantum embedding method for SDEs that simplifies quantum circuit design and discusses its potential for quantum speed-up in expectation value calculations.

Findings

Successfully simulates various stochastic processes numerically.

Demonstrates the feasibility of quantum embedding for SDEs.

Discusses potential for quantum speed-up in expectation calculations.

Abstract

Stochastic differential equations (SDEs), which models uncertain phenomena as the time evolution of random variables, are exploited in various fields of natural and social sciences such as finance. Since SDEs rarely admit analytical solutions and must usually be solved numerically with huge classical-computational resources in practical applications, there is strong motivation to use quantum computation to accelerate the calculation. Here, we propose a quantum-classical hybrid algorithm that solves SDEs based on variational quantum simulation (VQS). We first approximate the target SDE by a trinomial tree structure with discretization and then formulate it as the time-evolution of a quantum state embedding the probability distributions of the SDE variables. We embed the probability distribution directly in the amplitudes of the quantum state while the previous studies did the square-root of the probability distribution in the amplitudes. Our embedding enables us to construct simple quantum circuits that simulate the time-evolution of the state for general SDEs. We also develop a scheme to compute the expectation values of the SDE variables and discuss whether our scheme can achieve quantum speed-up for the expectation-value evaluations of the SDE variables. Finally, we numerically validate our algorithm by simulating several types of stochastic processes. Our proposal provides a new direction for simulating SDEs on quantum computers.