Solving Computational Fluid Dynamic Problems Using Quantum Computing

High-performance computing (HPC) refers to the use of advanced computational models and technologies to significantly enhance the performance of processing highly complex problems. HPC has evolved dramatically since the first supercomputer was commissioned in the 1960s. Nowadays, HPC is characterized by the pursuit of exascale computing, i.e., the ability to perform 10¹⁸ calculations per second. In November 2024, Lawrence Livermore National Laboratory, California announced the new El Capitan System as the most powerful exascale supercomputer, capable of performing over 1.742 exaFlops. Over the past few years, HPC platforms have evolved from CPU-centric architecture to GPU-accelerated systems. Such computing capability empowers researchers to tackle a diverse array of scientific applications across numerous domains, including stock market prediction, task scheduling, weather forecasting, gene sequencing, drug discovery, and Computational Fluid Dynamics (CFD) (Netto et al., 2018; Jamshed, 2015). CFD is an applied science field that is concerned with the simulation and analysis of the physics of fluid flow using computational models and technologies. CFD finds extensive applications in diverse industries and scientific disciplines, including aircraft design optimization to facilitate the analysis of aerodynamic performance, drag reduction, and fuel efficiency enhancement (Moin and Apte, 2006). In addition, CFD plays a crucial role in oil reservoir simulation and recovery (Britto et al. 2020). Overall, CFD serves as a powerful tool for understanding and predicting fluid flow phenomena, enabling engineers and scientists to make informed decisions, optimize designs, and improve the performance of various systems and processes. Recently, Quantum Computing has emerged as a new computing approach that leverages quantum physics phenomena such as superposition and entanglement to provide computational speedup over classical computers. With the rapid progress of quantum computing, it is projected that future HPC platforms are venturing into Quantum Processing Unit (QPU)-based systems to tackle previously unsolvable problems. Several researchers have studied the potential use of quantum computing in solving complex CFD problems (Gaitan, 2020; Jaksch et al. 2023). Quantum computing can offer several advantages in CFD applications. First, quantum computing can address the exponential growth of computational complexity associated with high-resolution and turbulent flow simulations. Accordingly, more accurate and detailed simulations can be performed. For example, Berry et al. (2014) presented quantum algorithms that can effectively handle large-scale linear algebraic calculations, such as solving sparse matrix equations and eigenvalue problems, which are fundamental in CFD. Furthermore, quantum computers have the potential to accelerate optimization algorithms that facilitate faster design and optimization of fluid systems (Preskill, 2018). While quantum computing is still in its nascent stage, ongoing research in quantum hardware and algorithms demonstrates huge potential for future applications in solving complex CFD problems.