A Hybrid Quantum-Classical Particle-in-Cell Method for Plasma Simulations

TL;DR

This paper introduces a hybrid quantum-classical Particle-in-Cell method for plasma simulations, utilizing a quantum computer simulator for the electrostatic field solver, demonstrating comparable accuracy to classical methods and exploring the potential of quantum computing in plasma physics.

Contribution

The paper presents a novel hybrid quantum-classical PIC method with a quantum Poisson solver trained via neural networks, advancing quantum applications in plasma simulation.

Findings

Quantum Poisson solver achieves accuracy comparable to classical methods.

Hybrid approach provides insights into quantum computing's feasibility for plasma simulations.

Discussion of current quantum computational overhead and potential advantages.

Abstract

We present a hybrid quantum-classical electrostatic Particle-in-Cell (PIC) method, where the electrostatic field Poisson solver is implemented on a quantum computer simulator using a hybrid classical-quantum Neural Network (HNN) using data-driven and physics-informed learning approaches. The HNN is trained on classical PIC simulation results and executed via a PennyLane quantum simulator. The remaining computational steps, including particle motion and field interpolation, are performed on a classical system. To evaluate the accuracy and computational cost of this hybrid approach, we test the hybrid quantum-classical electrostatic PIC against the two-stream instability, a standard benchmark in plasma physics. Our results show that the quantum Poisson solver achieves comparable accuracy to classical methods. It also provides insights into the feasibility of using quantum computing and HNNs for plasma simulations. We also discuss the computational overhead associated with current quantum computer simulators, showing the challenges and potential advantages of hybrid quantum-classical numerical methods.