← Back to all news
arXiv Machine Learning By Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy, Aaron Goldgewert, Gal Shaviner, Boris Shragner, Steven H. Frankel

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Related stories

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
diffusionbenchmarkssafety
More like this →
arXiv AI
Jun 3

Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation

arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.

By Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada, Martin Roetteler, Iordanis Kerenidis
benchmarks
More like this →