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EAGE Annual Conference & Exhibition

What can Quantum Computing Mean for Geoscience?

M. Dukalski

Published Jan 1, 2021• 0 citations
Publisher

Abstract

Summary In the recent years quantum technologies have matured enough that modern quantum computers outperform their classical counterparts at some very special computational tasks. It is however not entirely clear what kind of practical problems can be solved using present or future quantum computers. Here we will try to give a first attempt at answering this question with focus on optimization problems in geoscience.

DOI
10.3997/2214-4609.202012090

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Summary In the recent years quantum technologies have matured enough that modern quantum computers outperform their classical counterparts at some very special computational tasks. It is however not entirely clear what kind of practical problems can be solved using present or future quantum computers. Here we will try to give a first attempt at answering this question with focus on optimization problems in geoscience.
The computational demands of geoscientific workflows are rapidly increasing in both complexity and scale, pushing the limits of classical computing. Quantum computing has emerged as a promising paradigm to address some of these challenges. This paper surveys the growing intersection between quantum computing and energy geosciences, highlighting recent developments and initiatives. To illustrate the practical relevance of this emerging field, we present a case study on seismic traveltime inversion using a variational quantum circuit. In this example, a hybrid quantum-classical workflow is used to reconstruct velocity models from noisy traveltime data, demonstrating how quantum circuits can solve geophysical inverse problems. While modest in scale, the study offers insights into the formulation and implementation of quantumassisted inversion. As quantum hardware advances and algorithmic frameworks mature, quantum computing is poised to become a transformative tool in the computational landscape of energy geosciences.
Quantum computing is necessary to be described as a powerful tool is the new generation of computational power that works based on the principles of quantum mechanics, allowing the solving of problems that are intractable for classical computers. Due to the utilization of such phenomena as superposition, quantum entanglement and quantum interference, quantum computers are said to be able to solve problems in such areas as cryptography, artificial intelligence, optimization problems and material sciences, among others. This paper attempts to examine the basic concepts of quantum computing while focusing on their different aspects and their potential impacts. This study its experience and potential in its current form and in future applications through examples, illustrations, and theoretical discussion. Specific problems, including error correction, hardware scalability, and requirements for resources to address, make the paper a comprehensive overview of the quantum computing potential and weaknesses. Moving towards practical examples, the discussion covers all aspects of the book, providing readers with a holistic understanding of how quantum computing is going to change technological paradigms in the forthcoming decades.
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.
In recent years, Machine Learning (ML) has started to be ubiquitously applied to practically most of the human activity domains. Although traditional, or classical machine learning (CML) approaches are useful in solving many complex tasks, there are still many challenges that such approaches are facing. One issue is the limitation in the processing speed of current silicon technology based computers, which made researchers look to the underlying quantum theory principles and try to run complex quantum computing experiments. In future, in order to develop more complex artificial intelligence systems, we will have to process huge amounts of data at high speed, and the existing classical computing will probably not serve well this purpose, due to silicon technology limitations. A different technology that can handle huge volumes of data and at high speed is needed. In recent years, the progress in quantum computing research seems to provide hopeful answers to overcome the speed processing barrier. This is very important for the training of many computational intensive machine learning models. The latest advancements in quantum technology appear to be promising, which can boost the field of machine learning overall. In this chapter, we discuss the transition from classical machine learning to quantum machine learning (QML) and explore the recent progress in this domain. QML is not only associated with the development of high-performance machine learning algorithms that can run on a quantum computer with significant performance improvements but also has a very diverse meaning in other aspects. The chapter tried to touch those aspects in brief too, but the main focus is on the advancements in the field of developing machine learning algorithms that will run on a quantum computer.