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EAGE Workshop on High Performance Computing for Upstream

Toward an application of quantum computing in geophysics

M. Dukalski

Published Jan 1, 2021• 3 citations
Publisher

Abstract

Summary Quantum computing offers a theoretical speed-up (in terms computational complexity) when performing certain computational tasks. Successful inclusion of quantum computing units in existing HPC solutions is contingent on identifying appropriate and realistic use-cases, of which, to date, there are very few candidates. This is particularly true for upstream business. This is because, there are few quantum algorithms, each coming with a number of caveats which ought to be carefully studied. We suggest a potential simple use-case in geophysics and 1D inverse scattering theory and provide detailed analysis of the requirements needed to be met in order to achieve the promised speed-up.

DOI
10.3997/2214-4609.2021612005

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Summary Quantum computing offers a theoretical speed-up (in terms computational complexity) when performing certain computational tasks. Successful inclusion of quantum computing units in existing HPC solutions is contingent on identifying appropriate and realistic use-cases, of which, to date, there are very few candidates. This is particularly true for upstream business. This is because, there are few quantum algorithms, each coming with a number of caveats which ought to be carefully studied. We suggest a potential simple use-case in geophysics and 1D inverse scattering theory and provide detailed analysis of the requirements needed to be met in order to achieve the promised speed-up.
Accurate modeling of seismic wave propagation in the subsurface of the earth is essential for understanding earthquake dynamics, characterizing seismic hazards on global scales and hydrocarbon reservoir exploration and monitoring on local scales. These are among the most challenging computational problems in geoscience. Despite algorithmic advances and the increasingly powerful computational resources currently available, including fast CPUs, GPUs and large volumes of computer memory, there are still daunting computational challenges in simulating 3D seismic wave propagation in complex earth environments. Recent advances in quantum computing are suggestive that geoscience may soon begin to benefit from this promising field. For example, Finite Difference (FD) modeling is the most widely used method to simulate seismic wave propagation. In the frequency domain, FD methods reduce solutions of the wave equation into systems of linear equations; such systems are just the type that quantum algorithms may be capable of solving with exponential speedup, in comparison with classical algorithms. For the computational geophysicist, to prepare to take advantage of these speed-ups, which could arrive in as few as 5-10 years, the tasks at hand are (1) to become familiar with the logic and concepts associated with quantum computing, and (2) to map our key computational algorithms (e.g., frequency domain FD) to this domain. Presentation Date: Monday, October 15, 2018 Start Time: 1:50:00 PM Location: 204B (Anaheim Convention Center) Presentation Type: Oral
Summary Advancements in nano-fabrication as well as improved control over quantum states has brought quantum technologies to the technological readiness level where large business took increased interest in this technology and started playing an active role in its development. Perhaps as a result of that, quantum computing has received a lot of media attention in the recent years, which makes it very important to understand what quantum computers (once sufficiently powerful one gets built) could or could not do and separate science from the hype which starts to surround this field. In this talk I will attempt to address these opportunities as well as challenges and suggest a few possible areas within upstream business where quantum computing could make an impact.
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.
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
Summary In this work we closely examine the overlap between (1) the computationally challenging problems in geophysical applications in the upstream business and (2) the capabilities of quantum annealing (QA) quantum processing units (QPUs). We analyze the strengths and limitations of the latter and we explain how and why QPUs need to be assisted with CPUs and GPUs to solve combinatorial optimization problems at industrial scale. We illustrate the discussion with three potential use cases: stack power maximization for residual statics estimation, “ray tracing” and compressive sensing.