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arXiv:2403.11989 · Journal of Plasma Physics

Encoding of linear kinetic plasma problems in quantum circuits via data compression

I. Novikau, I. Y. Dodin, Edward A. Startsev

Published Aug 1, 2024• 5 citations
arXivHTMLPDF

Abstract

We propose an algorithm for encoding linear kinetic plasma problems in quantum circuits. The focus is on modelling electrostatic linear waves in a one-dimensional Maxwellian electron plasma. The waves are described by the linearized Vlasov–Ampère system with a spatially localized external current that drives plasma oscillations. This system is formulated as a boundary-value problem and cast in the form of a linear vector equation $\boldsymbol {A}{\boldsymbol{\psi} } = \boldsymbol {b}$ to be solved by using the quantum signal processing algorithm. The latter requires encoding of matrix $\boldsymbol {A}$ in a quantum circuit as a sub-block of a unitary matrix. We propose how to encode $\boldsymbol {A}$ in a circuit in a compressed form and discuss how the resulting circuit scales with the problem size and the desired precision.

DOI
10.1017/s0022377824000795

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Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high-resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to solve partial differential equations with exponential speed-up, provided that the solution can be compressed and efficiently represented as a MPS within some tolerable error threshold. In this work, we explore the practicality of MPS methods for solving the Vlasov-Poisson equations for systems with one coordinate in space and one coordinate in velocity, and find that important features of linear and nonlinear dynamics, such as damping or growth rates and saturation amplitudes, can be captured while compressing the solution significantly. Furthermore, by comparing the performance of different mappings of the distribution functions onto the MPS, we develop an intuition of the MPS representation and its behavior in the context of solving the Vlasov-Poisson equations, which will be useful for extending these methods to higher-dimensional problems.