Quantum computing has great potential in speeding up many problems. Rather than stepping “down” from a classical Newtonian realm into the more complicated quantum realm we use the same processes as the phenomena being researched. In geoscience, quantum computing has many potential applications. For example, quantum computing can be used for simulations of radiometric dating. By simulating the decomposition of atoms, a better idea of how these decompose can be created. Simulating typical, unaccelerated decomposition would be the first step in this area of research. This can be done by creating a qbit (quantum bit) for each atom and connecting them such that if the atom higher in the chain decomposes, the next one down decomposes. This algorithm will likely not provide a quantum speedup by itself. However by embedding it in a simulated crystal (Xia 2020, Cai et al 2020), radiohalos and fission tracks could be studied. This could also assist in the study of accelerated nuclear decay. The flood heat problem could also be an interesting study. Similarities have been noted between the thermal properties of objects and the noise on a quantum device. Most study in this area has been focused on improving quantum computers (Sinha et al. 2022) but it could be used to simulate the systems of the earth under extreme conditions (Casalegno et al. 1999). There is also work being done on using quantum computing to speed up or improve computational fluid dynamics programs (Gaitan 2020, Steijl 2019, Lin et al. 2009). Unlike the other subjects in this proposal, this relies on a quantum computer’s ability to solve differential equations more effectively than a classical computer. It could allow for larger scale or more fine-grained simulations of sediment flows. It is known that there are useful algorithms that provide a speedup for similar problems, or have the potential to provide speedup when our quantum computers improve sufficiently. Further research is needed to determine which of these areas of study are included in the subset of problems that can be better solved by quantum methods.
Quantum computing has great potential in speeding up many problems. Rather than stepping “down” from a classical Newtonian realm into the more complicated quantum realm we use the same processes as the phenomena being researched. In geoscience, quantum computing has many potential applications. For example, quantum computing can be used for simulations of radiometric dating. By simulating the decomposition of atoms, a better idea of how these decompose can be created. Simulating typical, unaccelerated decomposition would be the first step in this area of research. This can be done by creating a qbit (quantum bit) for each atom and connecting them such that if the atom higher in the chain decomposes, the next one down decomposes. This algorithm will likely not provide a quantum speedup by itself. However by embedding it in a simulated crystal (Xia 2020, Cai et al 2020), radiohalos and fission tracks could be studied. This could also assist in the study of accelerated nuclear decay. The flood heat problem could also be an interesting study. Similarities have been noted between the thermal properties of objects and the noise on a quantum device. Most study in this area has been focused on improving quantum computers (Sinha et al. 2022) but it could be used to simulate the systems of the earth under extreme conditions (Casalegno et al. 1999). There is also work being done on using quantum computing to speed up or improve computational fluid dynamics programs (Gaitan 2020, Steijl 2019, Lin et al. 2009). Unlike the other subjects in this proposal, this relies on a quantum computer’s ability to solve differential equations more effectively than a classical computer. It could allow for larger scale or more fine-grained simulations of sediment flows. It is known that there are useful algorithms that provide a speedup for similar problems, or have the potential to provide speedup when our quantum computers improve sufficiently. Further research is needed to determine which of these areas of study are included in the subset of problems that can be better solved by quantum methods.
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
Quantum computing represents a revolutionary leap in computational power, leveraging the principles of quantum mechanics to process information in fundamentally new ways. Unlike classical computers, which use bits to represent data as 0s or 1s, quantum computers use quantum bits or qubits, enabling the phenomena of superposition and entanglement to perform complex calculations more efficiently (Nielsen & Chuang, 2010). This article explores the basic principles of quantum computing, the different models and algorithms that have been developed, and the current state of quantum hardware. The power of quantum computing lies in its ability to solve certain types of problems exponentially faster than classical computers. For instance, Shor's algorithm can factorize large integers in polynomial time, posing a significant threat to classical encryption methods (Shor, 1997). Similarly, Grover's algorithm provides a quadratic speedup for unstructured search problems, showcasing the potential for dramatic improvements in database search capabilities (Grover, 1996). These algorithms exemplify how quantum computing can revolutionize fields that rely on complex computations. Quantum hardware, however, is still in its nascent stage, with significant challenges to be overcome. Current quantum computers, such as those developed by IBM and Google, have demonstrated quantum supremacy but are limited by issues of qubit coherence and error rates (Arute et al., 2019). Quantum error correction and the development of more stable qubit technologies are critical areas of ongoing research (Preskill, 2018). Despite these challenges, recent advancements in quantum hardware and algorithms continue to drive the field forward, promising transformative impacts on industries such as cryptography, drug discovery, and artificial intelligence. As quantum computing continues to advance, it holds the promise of solving problems that are currently intractable for classical computers, paving the way for unprecedented technological and scientific advancements (Shor, 1997; Grover, 1996). This article provides a comprehensive overview of the current landscape of quantum computing, discussing its foundational principles, key algorithms, hardware challenges, and potential applications. By understanding these aspects, we can better appreciate the profound implications of quantum computing for the future of technology and science.
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.
In the last few years, quantum computing has evolved as the next big technological breakthrough. Many big companies are actively researching this area and have built open-source frameworks which can be used to develop solutions for problems that connect be solved by classical computers. One such problem is the solution of Navier-Stokes equations which takes exponential time for simulation but using quantum computers, they can be solved in polynomial time. But the current state of quantum computation does not enable everyone to test their implementation on a real quantum computer. In this situation, a simulator enables rapid prototype and testing techniques and algorithms for when the real quantum computer is ready. This paper focuses on simulating fluid flow algorithms using a quantum framework, such as Navier- Stokes equations, with the lattice Boltzmann method (LBM) used to numerically solve the relevant system of equations on both CPU and GPU to speed up the process. The quantum framework used in this paper is Qiskit, an open-source software development kit created by IBM that allows developers to create and test quantum algorithms. NVIDIA cuQuantum is an SDK of optimized libraries and tools for expediting quantum computing operations used in this paper to solve the fundamental problem of modeling large-size quantum circuits using the power of GPU
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.