14-17 May 2018
New Orleans
US/Central timezone

Quantum-computational approach to discrete tomography for porous media

15 May 2018, 15:13
New Orleans

New Orleans

Oral 20 Minutes MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement Parallel 5-D


Daniel O'Malley (Los Alamos National Laboratory)


Making predictions about flow and transport in a porous medium requires knowledge of the heterogeneous properties of the porous medium such as permeability. Computational methods for inverse analysis are commonly used to infer these properties from quantities that are more readily observable (such as hydraulic head in a hydrologic context). We present a method for computational inverse analysis that utilizes a type of quantum computer called a quantum annealer. While quantum computing is in an early stage compared to classical computing, we demonstrate that it is sufficiently developed that it can be used to segment a porous medium into regions of high and low permeability. We utilize a D-Wave 2X quantum annealer to solve 1D and 2D inverse problems that, while small by modern standards, are similar in size and sometimes larger than similar inverse problems that were solved with early classical computers. Our results and the rapid progress being made with quantum computing hardware indicate that the era of quantum-computation in porous media may not be too far in the future.

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Primary author

Daniel O'Malley (Los Alamos National Laboratory)

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