Quantum Algorithm for a Stochastic Multicloud Model

TL;DR

This paper presents a quantum computing algorithm applied to a stochastic multicloud atmospheric model, demonstrating comparable results to classical methods and highlighting quantum computing's potential in climate science.

Contribution

It introduces a quantum algorithm for atmospheric stochastic modeling and evaluates its effectiveness using a quantum simulator, bridging quantum computing and climate science.

Findings

Quantum algorithm achieves results comparable to classical Monte Carlo methods.

Quantum states' probabilistic outputs can replicate stochastic atmospheric behaviors.

Quantum computing shows potential for complex climate and oceanic simulations.

Abstract

Quantum computers have attracted much attention in recent years. This is because the development of the actual quantum machine is accelerating. Research on how to use quantum computers is active in the fields such as quantum chemistry and machine learning, where vast amounts of computation are required. However, in weather and climate simulations, less research has been done despite similar computational demands. In this study, a quantum computing algorithm is applied to a problem of the atmospheric science. The effectiveness of the proposed algorithm is evaluated using a quantum simulator. The results show that it can achieve the same simulations as a conventional algorithm designed for classical computers. More specifically, the stochastically fluctuating behavior of a multi-cloud model was obtained using classical Monte Carlo method, and comparable results are also achieved by utilizing probabilistic outputs of computed quantum states. Our results show that quantum computers have a potential to be useful for the atmospheric and oceanic science, in which stochasticity is widely inherent.