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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2006.06719 |
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| _version_ | 1866918147525705728 |
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| author | Au-Yeung, Rhonda Williams, Anthony J. Kendon, Viv M. Lind, Steven J. |
| author_facet | Au-Yeung, Rhonda Williams, Anthony J. Kendon, Viv M. Lind, Steven J. |
| contents | We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_06719 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Quantum Algorithm for Smoothed Particle Hydrodynamics Au-Yeung, Rhonda Williams, Anthony J. Kendon, Viv M. Lind, Steven J. Quantum Physics Computational Physics We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers. |
| title | Quantum Algorithm for Smoothed Particle Hydrodynamics |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2006.06719 |