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

2025-05-14

AI Summary

This work addresses the computational bottleneck of electrostatic Poisson solvers in plasma simulations by proposing a hybrid quantum–classical particle-in-cell (PIC) method: a physics-informed and data-driven hybrid neural network (HNN) replaces the conventional Poisson solver, while the remainder of the PIC workflow executes on classical hardware. It introduces, for the first time, physics-informed neural networks (PINNs) into differentiable quantum circuits, yielding an end-to-end trainable quantum Poisson solver. Feasibility is validated on the two-stream instability benchmark. Experiments demonstrate that the quantum solver achieves accuracy comparable to classical solvers, while quantifying resource overhead on current noisy intermediate-scale quantum (NISQ) hardware—specifically, PennyLane-based simulators. This work establishes a novel paradigm and foundational methodology for efficient plasma simulation on future fault-tolerant quantum hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
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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.

Problem

• Hybrid quantum-classical PIC method for plasma simulations
• Quantum Poisson solver accuracy vs classical methods
• Feasibility of quantum computing in plasma simulations

Innovation

• Hybrid quantum-classical PIC method for plasma
• Quantum Poisson solver using hybrid neural network
• PennyLane simulator for quantum computational steps