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Quantized tensor networks for solving the Vlasov–Maxwell equations

Part of: Plasma and quantum information sciences

Published online by Cambridge University Press:  18 September 2024

Erika Ye Open the ORCID record for Erika Ye [Opens in a new window]  and
Nuno F. Loureiro Open the ORCID record for Nuno F. Loureiro [Opens in a new window]
Erika Ye*
Affiliation:
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Nuno F. Loureiro
Affiliation:
Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
†Email address for correspondence: erikaye@lbl.gov
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Abstract

The Vlasov–Maxwell equations provide an ab initio description of collisionless plasmas, but solving them is often impractical because of the wide range of spatial and temporal scales that must be resolved and the high dimensionality of the problem. In this work, we present a quantum-inspired semi-implicit Vlasov–Maxwell solver that uses the quantized tensor network (QTN) framework. With this QTN solver, the cost of grid-based numerical simulation of size $N$ is reduced from $O(N)$ to $O(\text {poly}(D))$, where $D$ is the ‘rank’ or ‘bond dimension’ of the QTN and is typically set to be much smaller than $N$. We find that for the five-dimensional test problems considered here, a modest $D=64$ appears to be sufficient for capturing the expected physics despite the simulations using a total of $N=2^{36}$ grid points, which would require $D=2^{18}$ for full-rank calculations. Additionally, we observe that a QTN time evolution scheme based on the Dirac–Frenkel variational principle allows one to use somewhat larger time steps than prescribed by the Courant–Friedrichs–Lewy constraint. As such, this work demonstrates that the QTN format is a promising means of approximately solving the Vlasov–Maxwell equations with significantly reduced cost.

Keywords

plasma simulationplasma dynamics
Type
Research Article
Information
Journal of Plasma Physics , Volume 90 , Issue 3 , June 2024 , 805900301
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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