Quantum approaches for particle-in-cell codes
Abstract
Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge. In fluid dynamics, quantum-inspired methods based on tensor-network techniques have recently emerged as a promising new paradigm, offering large degrees of data compression on conventional hardware and a natural pathway towards solving exponentially large problems on quantum computers. Early studies suggest that tensor-network methods may also help overcome key bottlenecks in computational plasma dynamics, particularly for industry-relevant scenarios where conventional simulations remain costly. In this work, we present recent progress in developing tensor-network methods compared to standard particle-in-cell codes and discuss their capability to overcome the bottlenecks of conventional approaches. We further extend the tensor-network formulation to support the insertion of electromagnetic fields at arbitrary spatial locations. This capability is demonstrated through the injection of a laser field to accelerate a plasma, providing a key step towards tensor-network-based simulation of externally driven plasma systems.
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BibTeX
@misc{connor2026quantum,
title = {{Quantum approaches for particle-in-cell codes}},
author = {Ryan J. J. Connor and Andrew J. Daley and Callum W. Duncan and Preetma Soin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03254},
eprint = {2610.03254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03254},
}
RIS
TY - UNPB
TI - Quantum approaches for particle-in-cell codes
AU - Ryan J. J. Connor
AU - Andrew J. Daley
AU - Callum W. Duncan
AU - Preetma Soin
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03254
UR - https://arxiv.org/abs/2610.03254
AB - Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge. In fluid dynamics, quantum-inspired methods based on tensor-network techniques have recently emerged as a promising new paradigm, offering large degrees of data compression on conventional hardware and a natural pathway towards solving exponentially large problems on quantum computers. Early studies suggest that tensor-network methods may also help overcome key bottlenecks in computational plasma dynamics, particularly for industry-relevant scenarios where conventional simulations remain costly. In this work, we present recent progress in developing tensor-network methods compared to standard particle-in-cell codes and discuss their capability to overcome the bottlenecks of conventional approaches. We further extend the tensor-network formulation to support the insertion of electromagnetic fields at arbitrary spatial locations. This capability is demonstrated through the injection of a laser field to accelerate a plasma, providing a key step towards tensor-network-based simulation of externally driven plasma systems.
ER -