To provide new disruptive means to perform computations with increased complexity, high-performance computing (HPC) will need a radical departure from the conventional classical computing platform. Quantum computing (QC), which makes use of the unusual physical properties of microscopic objects to process information, is a particularly promising candidate to be this disruptive technology for a range of computational problems. There is growing evidence that quantum information science (QIS) in general and QC in particular are approaching an inflection point, with significant opportunities and challenges for various scientific and engineering fields. In the fields of aeroscience and engineering, there are important implications for various national missions and responsibilities, including mission planning, autonomy, air space management, and material design and advancing current HPC applications such as computational materials research, computational fluid dynamics (CFD), combustion, aerothermodynamics, and multidisciplinary design and optimization (MDAO).
To provide new disruptive means to perform computations with increased complexity, high-performance computing (HPC) will need a radical departure from the conventional classical computing platform. Quantum computing (QC), which makes use of the unusual physical properties of microscopic objects to process information, is a particularly promising candidate to be this disruptive technology for a range of computational problems. There is growing evidence that quantum information science (QIS) in general and QC in particular are approaching an inflection point, with significant opportunities and challenges for various scientific and engineering fields. In the fields of aeroscience and engineering, there are important implications for various national missions and responsibilities, including mission planning, autonomy, air space management, and material design and advancing current HPC applications such as computational materials research, computational fluid dynamics (CFD), combustion, aerothermodynamics, and multidisciplinary design and optimization (MDAO).
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.
Quantum computing promises to provide the next step up in computational power for diverse application areas. In this review, we examine the science behind the quantum hype, and the breakthroughs required to achieve true quantum advantage in real world applications. Areas that are likely to have the greatest impact on high performance computing (HPC) include simulation of quantum systems, optimization, and machine learning. We draw our examples from electronic structure calculations and computational fluid dynamics which account for a large fraction of current scientific and engineering use of HPC. Potential challenges include encoding and decoding classical data for quantum devices, and mismatched clock speeds between classical and quantum processors. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of 'green' materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.
Quantum computing represents a groundbreaking advancement with significant implications for engineering and science. This article delves into the fundamental principles of quantum computing and highlights major technological breakthroughs driving its progress. It examines the influence of quantum computing on engineering, emphasizing its potential to address complex problems with unmatched speed and efficiency. In the realm of scientific research, quantum computing offers transformative opportunities in areas such as materials science, cryptography, and drug discovery. Despite its potential, quantum computing faces considerable challenges, including hardware stability, high error rates, and scalability issues. The conclusion discusses future prospects, underscoring the importance of ongoing research and collaboration to overcome these challenges and fully leverage the potential of quantum computing for advancements in engineering and science.
This document is meant to review and discuss the possible applications of Quantum computing to computational fluid dynamics (CFD) problems.A review of the current state-of-the-art of quantum computing applied to computational fluid dynamics has been carried out, highlining how the technology is still in an early stage of development but very promising.Quantum computing is a type of computation that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, to perform calculations.The devices that perform quantum computations are known as quantum computers.Quantum Computing is currently a very active field of study and development, with expected applications in various fields, including engineering problems.Indeed, several approaches have been proposed for the solutions of CFD problems with the use of quantum algorithms and / or quantum computers.Possible approaches, applicable to compact heat exchangers used in aeronautics, include the development of quantum algorithms, e.g., based on the solutions of Navier Stokes equations or the Lattice Boltzmann Method., the design of an "analog" quantum computer able to mimic the thermal fluid system under investigation, and, finally, the implementation of a scientific machine learning approach.
The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems.
There are signs that we are approaching a technical revolution that might take humankind’s computational capabilities to a new level. Quantum Computing leverages the principles of quantum mechanics, enabling the computational power to solve specific problems currently intractable for classical computers (e.g., cracking cryptographic keys). Since we are getting to a point where we can no longer build smaller, more powerful, more efficient devices with conventional methodologies, we need to think of new ways for technological progress. The manufacturing of microchips is reaching its limits in the use of traditional architectures. Hence, new and more advanced technologies need to address the challenges of increasing energy consumption and data processing. Connectivity, scalable architectures and concepts like Cloud Computing and Software-as-a-Service have already helped to scale computational-intense applications in the last years and have attracted the interest of IS researchers (e.g., Benlian et al. 2009 ; Benlian et al. 2018 ; Messerschmidt and Hinz 2013 ). Looking ahead to the next generation, computing will comprise technologies that enable high-performance applications far beyond today’s possibilities. One prominent example with a disruptive potential is Quantum Computing. While there are abundant open questions and directions for further developments, researchers in this area have made substantial progress in the last decade, indicating that, after medical imaging, lasers, and superconductors, another quantum-technological innovation might be approaching. We, therefore, intend to provide an overview of the current state of Quantum Computing research and outline how this new technology might stimulate research in Information Systems.