High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.
High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
Quantum-centric supercomputing has the potential to revolutionize various fields by offering unprecedented computational power to tackle the world's most pressing problems. This research survey aims to provide a comprehensive understanding of the fundamentals of quantum computing, the current state of quantum-centric supercomputers, and their potential applications in solving global challenges. We explore the basic concepts underlying quantum computing, including qubits, superposition, entanglement, and quantum algorithms. Furthermore, we examine the existing and proposed architectures of quantum-centric supercomputers, as well as recent technological advancements and breakthroughs. The paper also discusses the application of these advanced computational systems in areas such as climate modeling, drug discovery, cryptography, and artificial intelligence. Finally, we address the future directions and opportunities for quantum-centric supercomputing, including technological advances, research collaborations, and policy and ethical considerations. Our findings underscore the transformative potential of quantum-centric supercomputing and the need for continued research and development to fully harness its capabilities for addressing the world's most critical problems.
Quantum computers have demonstrated utility in simulating quantum systems beyond brute-force classical approaches. As the community builds on these demonstrations to explore using quantum computing for applied research, algorithms and workflows have emerged that require leveraging both quantum computers and classical high-performance computing (HPC) systems to scale applications, especially in chemistry and materials, beyond what either system can simulate alone. Today, these disparate systems operate in isolation, forcing users to manually orchestrate workloads, coordinate job scheduling, and transfer data between systems -- a cumbersome process that hinders productivity and severely limits rapid algorithmic exploration. These challenges motivate the need for flexible and high-performance Quantum-Centric Supercomputing (QCSC) systems that integrate Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) to accelerate discovery of such algorithms across applications. These systems will be co-designed across quantum and classical HPC infrastructure, middleware, and application layers to accelerate the adoption of quantum computing for solving critical computational problems. We envision QCSC evolution through three distinct phases: (1) quantum systems as specialized compute offload engines within existing HPC complexes; (2) heterogeneous quantum and classical HPC systems coupled through advanced middleware, enabling seamless execution of hybrid quantum-classical algorithms; and (3) fully co-designed heterogeneous quantum-HPC systems for hybrid computational workflows. This article presents a reference architecture and roadmap for these QCSC systems.
Abstract: This article presents a comprehensive review of the integration of quantum computing with High-Performance Computing (HPC), examining current trends, challenges, and future directions. We explore the fundamental principles of quantum computing and their relevance to HPC, followed by an analysis of state-of-the-art quantum algorithms and hybrid quantum-classical systems. The article addresses key technical challenges in hardware and software integration, as well as scalability and stability issues in quantum-enhanced HPC systems. Through a series of case studies, we demonstrate the practical applications and potential impact of quantum-HPC integration across various industries, including finance, drug discovery, and logistics. Our findings highlight the transformative potential of quantum computing in HPC, suggesting unprecedented improvements in computational speed and efficiency. The article concludes with an overview of emerging quantum technologies and identifies promising research opportunities, emphasizing the importance of collaboration between academia and industry. This work serves as a valuable resource for researchers, engineers, and industry professionals seeking to understand and leverage the synergy between quantum computing and HPC, paving the way for future innovations in computational capabilities
The deployment of many-body quantum chemistry methods onto massively parallel high-performance computing (HPC) platforms is reviewed. The particular focus is on highly accurate methods that have become popular in predictive description of chemical phenomena, such as the coupled-cluster method. The account of relevant literature is preceded by a discussion of the modern and near-future HPC landscape and the relevant computational traits of the many-body methods, in their canonical and reduced-scaling formulations, that underlie the challenges in their HPC realization.
I n this presentation we will first talk about the different classes of quantum algorithms currently in use. We will then discuss some hybrid (Quantum + Classical) algorithms in use today, notably the variational quantum algorithms (VQAs). The VQAs could be used in a wide range of applications. We will then discuss the limitations of these algorithms and the need for other approaches. Quantum centric supercomputing (QCSC) is an idea where Quantum algorithms in Quantum hardware work in tandem with classical algorithms in high-performance computers. We will discuss the different varieties of Quantum + HPC workloads. Finally, we will discuss a use-case where Quantum + HPC has been used to solve a chemistry problem. We will end the presentation showing IBM’s roadmap in achieving QCSC.