arXiv Machine Learning June 19, 2026 By Xiang Rao, Yuxuan Shen

Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs

Read the original on arXiv Machine Learning →

arXiv:2606. 20326v1 Announce Type: new Abstract: We develop QCPIKAN, the first quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations (PDEs).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics

arXiv:2608. 00850v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems.

By Fabio Pereira dos Santos, Renato Portugal, J\'ulio de Castro Vargas Fernandes, Lucas Timotheo Sanches

diffusion benchmarks safety

arXiv Machine Learning

1d ago

Kolmogorov-Arnold Networks for Free-Boundary Partial Differential Equations

arXiv:2610. 02084v1 Announce Type: cross Abstract: We study free-boundary problems within a physics-informed framework using Kolmogorov-Arnold network (KAN) approximations.

By Tan Phuong Dong Le

arXiv Machine Learning

Aug 11

Quantum-Classical Physics-Informed Kolmogorov-Arnold Networks for Solving Fuzzy Differential Equations

arXiv:2608. 08782v1 Announce Type: new Abstract: In this study, we propose a quantum-classical physics-informed Kolmogorov-Arnold network (QCPIKAN) dedicated to the solution of fuzzy differential equations.

By Xiang Rao, Yuxuan Shen

arXiv Machine Learning

Aug 11

Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework

arXiv:2604. 15645v2 Announce Type: replace Abstract: We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow.

By Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy, Aaron Goldgewert, Gal Shaviner, Boris Shragner, Steven H. Frankel

rag benchmarks

arXiv Machine Learning

Sep 22

Physics-Informed Classical and Quantum Neural Networks for One-Dimensional Schrodinger Eigenvalue Problems

arXiv:2609.22189v1 Announce Type: cross Abstract: The Schrodinger equation in one spatial dimension admits a small set of exactly solvable potentials that serve as natural proving grounds for any new...

By Tariq Mahmood, Waqas Arshad, Bilal Naseer, Alfredo Raya

rag benchmarks

arXiv Machine Learning

Jun 19

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System

arXiv:2606. 19754v1 Announce Type: new Abstract: Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems.

By Zhiwen Yu, Derong Yang, Liujian Zhang, Kaixiang Yang, Peilin Zhan, Jianmin Lv, Jane You, C. L. Philip Chen

One email a morning, machine-written

Related stories

Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics

Kolmogorov-Arnold Networks for Free-Boundary Partial Differential Equations

Quantum-Classical Physics-Informed Kolmogorov-Arnold Networks for Solving Fuzzy Differential Equations

Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework

Physics-Informed Classical and Quantum Neural Networks for One-Dimensional Schrodinger Eigenvalue Problems

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System