Quantum-Assisted Learning of Time-Dependent Parabolic PDEs

Fuente: arXiv
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Main Authors: Dehaghani, Nahid Binandeh, Tran, Ban, Aguiar, A. Pedro, Wisniewski, Rafal, Mengel, Susan
Format: Preprint
Published: 2025
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author Dehaghani, Nahid Binandeh
Tran, Ban
Aguiar, A. Pedro
Wisniewski, Rafal
Mengel, Susan
author_facet Dehaghani, Nahid Binandeh
Tran, Ban
Aguiar, A. Pedro
Wisniewski, Rafal
Mengel, Susan
contents We present a hybrid quantum-classical framework for solving general time-dependent parabolic partial differential equations (PDEs) using quantum variational circuits. Building on the QPINN approach, this method applies broadly to parabolic PDEs. To demonstrate its effectiveness, we focus on the 1D and 2D heat equations as representative examples and analyze its performance under constrained quantum resources. Our results show that the framework can accurately capture spatiotemporal dynamics, offering a promising direction for quantum-assisted scientific computing.
format Preprint
id arxiv_https___arxiv_org_abs_2508_17553
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Quantum-Assisted Learning of Time-Dependent Parabolic PDEs
Dehaghani, Nahid Binandeh
Tran, Ban
Aguiar, A. Pedro
Wisniewski, Rafal
Mengel, Susan
Quantum Physics
We present a hybrid quantum-classical framework for solving general time-dependent parabolic partial differential equations (PDEs) using quantum variational circuits. Building on the QPINN approach, this method applies broadly to parabolic PDEs. To demonstrate its effectiveness, we focus on the 1D and 2D heat equations as representative examples and analyze its performance under constrained quantum resources. Our results show that the framework can accurately capture spatiotemporal dynamics, offering a promising direction for quantum-assisted scientific computing.
title Quantum-Assisted Learning of Time-Dependent Parabolic PDEs
topic Quantum Physics
url https://arxiv.org/abs/2508.17553