An approach to seismic inversion with quantum annealing

Greer, Sarah; O’Malley, Daniel

doi:10.1190/segam2020-3424413.1

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…This is because this problem is not only hard but also easily implementable and scalable for today's quantum computers. We also show that (much like in the Greer and O'Malley [2020] example) classical computing will remain indispensable in theory and also in practice, in which all the imperfections from the interface of the classical and quantum worlds enter our solutions.…”

Section: Difficult Problems In Geophysicsmentioning

confidence: 77%

“…We will discuss two forms of quantum computing: so-called gate or circuit model (requiring algorithms) and AQC or QA (algorithm free and optimization focused). We will show how the range of applications in geophysics on the former is quite limited, whereas the latter can already boast a few use cases (delegating the optimization step of FWI on a toy model example being one of them; Greer and O'Malley, 2020). However, we wish to show that the SPM problem, such as for residual statics estimation, is an interesting candidate for being solved efficiently by a quantum computer.…”

Section: Difficult Problems In Geophysicsmentioning

confidence: 99%

“…In many cases, finding a second best optimum, one which is far away in some parameter space from the global one, is of little interest and could offer little improvement to, or worse, provide a false sense of certainty about the subsurface. O'Malley (2018), Greer and O'Malley (2020), and Souza et al (2022) show first examples of the types of problems that can be encountered in geophysics and solved using a quantum annealer, albeit with a lesser focus on addressing a realistic and business relevant problem with a multimodal objective function.…”

Section: Introductionmentioning

confidence: 99%

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Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

et al. 2023

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Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms and a use-case, however, proves to be a very challenging task especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We show that maximizing stack-power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space, and define a proper encoding to translate the problem-variables into qubit states. We show that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Lastly, we show that a generic, black-box, hybrid classical-quantum solver could also be used to solve stack-power maximization problems proximal to industrial relevance, and capable to surpassing deterministic solvers prone to cycle-skipping. A custom-build workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.

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Section: Difficult Problems In Geophysicsmentioning

confidence: 77%

Section: Difficult Problems In Geophysicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

et al. 2023

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“…To assess performance, a synthetic dataset was created. The use of a synthetic dataset is necessary in quantum computing benchmarking to ensure the validity of the algorithms used [40], [41], [42], [43], [44].…”

Section: B Quantum Annealing On a Synthetic Datasetmentioning

confidence: 99%

Power Network Optimization: A Quantum Approach

Colucci,

Linde,

Phillipson

2023

IEEE Access

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Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. In this paper, we show how to optimize such network costs using a quantum annealing approach. First, we define the Quadratic Unconstrained Binary Optimization (QUBO) problem for network partitioning. To achieve this, we introduce a method to translate inequality constraints with real-valued coefficients into approximate penalty functions. Next, we test the implementation on purely quantum and quantum-classical hybrid architectures. We then solve the problem using the D-Wave hybrid Constrained Quadratic Model (CQM) solver, the D-Wave hybrid Binary Quadratic Model (BQM) solver, as well as classical solvers available on Azure Quantum Cloud. Finally, we find that the value of the objective function obtained with the quantum-classical hybrid solvers is always lower compared to the classical approaches across a range of different problem sizes. This demonstrates that the quantum-classical hybrid methods outperform the classical methods in terms of solution quality.

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“…It is also important to mention that optimization techniques are widely used in seismic inversions, but usually classical algorithms get stuck in local minima. Previous works [13][14][15] have indicated that quantum annealing can be advantageous to solve seismic problems. However, the potential applications of quantum computing in Geoscience has so far been largely unexploited in the specialized literature.…”

mentioning

confidence: 99%

An Application of Quantum Annealing Computing to Seismic Inversion

Souza

Cirto²,

Martins³

et al. 2022

Front. Phys.

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Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational challenges with great interest in science and industry are the inversion problems. These kinds of numerical procedures can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. We compare the obtained results from the quantum computer to those derived from a classical algorithm. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.

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An approach to seismic inversion with quantum annealing

Cited by 8 publications

References 10 publications

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

Power Network Optimization: A Quantum Approach

An Application of Quantum Annealing Computing to Seismic Inversion

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