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Quantum algorithms for geologic fracture networks.

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New quantum algorithms show promise for simulating fractured flow, a complex problem intractable for classical computers. A noise-resilient quantum approach demonstrates practical performance for small to medium-sized systems.

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Area of Science:

  • Computational physics
  • Quantum computing
  • Fluid dynamics

Background:

  • Simulating subsurface flow requires solving large systems of equations, often necessitating coarse-graining to manage computational complexity.
  • Coarse-graining is inaccurate for fractured systems due to critical small-scale topology, including percolation thresholds.
  • Accurate modeling of fractured systems necessitates novel computational techniques.

Purpose of the Study:

  • Introduce and evaluate quantum algorithms for simulating fractured flow.
  • Assess the performance of quantum algorithms on current and future quantum hardware.
  • Address the limitations of classical coarse-graining methods for fractured media.

Main Methods:

  • Developed two quantum algorithms for solving linear systems relevant to fractured flow.
  • Evaluated the first algorithm on theoretical fault-tolerant quantum computers.
  • Experimentally tested and theoretically analyzed a second, noise-resilient quantum algorithm.

Main Results:

  • The first quantum algorithm shows theoretical potential but is currently hindered by hardware noise.
  • The second, noise-resilient algorithm demonstrates effective performance for systems of 10-1000 nodes.
  • Theoretical analysis and experimental results validate the second algorithm's capabilities.

Conclusions:

  • Quantum computing offers a promising avenue for simulating complex fractured flow systems.
  • Noise-resilient quantum algorithms are viable for current quantum hardware, with potential for scalability.
  • Future work will focus on quantum error mitigation and preconditioning to further enhance performance.