High-Performance Computing (HPC) can quickly process scientific data and perform complex calculations at extremely high speeds. A vast increase in HPC use across scientific communities is observed, especially in using parallel data science methods to speed-up scientific applications. HPC enables scaling up machine and deep learning algorithms that inherently solve optimization problems. More recently, the field of quantum machine learning evolved as another HPC related approach to speed-up data science methods. This paper will address primarily traditional HPC and partly the new quantum machine learning aspects, whereby the latter specifically focus on our experiences on using quantum annealing at the Juelich Supercomputing Centre (JSC). Quantum annealing is particularly effective for solving optimization problems like those that are inherent in machine learning methods. We contrast these new experiences with our lessons learned of using many parallel data science methods with a high number of Graphical Processing Units (GPUs). That includes modular supercomputers such as JUWELS, the fastest European supercomputer at the time of writing. Apart from practice and experience with HPC co-design applications, technical challenges and solutions are discussed, such as using interactive access via JupyterLab on typical batch-oriented HPC systems or enabling distributed training tools for deep learning on our HPC systems.
High-Performance Computing (HPC) can quickly process scientific data and perform complex calculations at extremely high speeds. A vast increase in HPC use across scientific communities is observed, especially in using parallel data science methods to speed-up scientific applications. HPC enables scaling up machine and deep learning algorithms that inherently solve optimization problems. More recently, the field of quantum machine learning evolved as another HPC related approach to speed-up data science methods. This paper will address primarily traditional HPC and partly the new quantum machine learning aspects, whereby the latter specifically focus on our experiences on using quantum annealing at the Juelich Supercomputing Centre (JSC). Quantum annealing is particularly effective for solving optimization problems like those that are inherent in machine learning methods. We contrast these new experiences with our lessons learned of using many parallel data science methods with a high number of Graphical Processing Units (GPUs). That includes modular supercomputers such as JUWELS, the fastest European supercomputer at the time of writing. Apart from practice and experience with HPC co-design applications, technical challenges and solutions are discussed, such as using interactive access via JupyterLab on typical batch-oriented HPC systems or enabling distributed training tools for deep learning on our HPC systems.
Quantum computing promises unprecedented possibilities for important computing tasks such as quantum simulations in chemistry and materials science or optimization and machine learning. With this potential, quantum computing is increasingly attracting interest from industry and scientific communities that use high performance computing (HPC) for their applications.Practical application requires the integration of quantum computers into existing HPC infrastructures in the form of quantum-classical hybrid computing models.The Jülich UNified Infrastructure for Quantum computing (JUNIQ), a manufacturer-independent quantum computing user facility established at the Jülich Supercomputing Centre (JSC) aims to address these needs.As an example, we present benchmarking results for the quantum approximate optimization algorithm (QAOA) emulated on a supercomputer and for the D-Wave quantum annealers for the tail assignment problem, a planning problem from aircraft industry.
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.
Use of AI/ML is associated with more and more computing power, which often necessitates the application to guzzle energy and take time to train. The paper presents a Quantum-Enhanced High-Performance Computing (Q-HPC) system that will combine traditional HPC units with quantum-assisted optimization to enhance the model training in addition to the predictive accuracy and energy efficiency. The framework that could be used to work with such large volumes of data is multi- GPU/CPU parallelization, and the optimization of parameters and hyperparameters can be implemented with the help of quantum-inspired algorithms. This will lead to a hybrid computation balancing dynamically the classical and quantum computations. Q-HPC was experimented with multiple AI/ML model types, such as convolutional networks, transformer models, graph neural networks and reinforcement learning agents, in which cases it was observed to traverse to the solution faster, more accurately and used less energy than the traditional HPC. It can also be said that the framework is dynamically adaptable and sustainable, i.e. it could be deployed to a massive diversifying range of tasks of AI/ML. The suggested model is a combination of the performance and scalability of the classical HPC with the optimization performance of quantum computing in order to create a new and valuable approach to the next generation AI. It deals with the issues of performance and environmental concerns of high-performance 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.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
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.