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

arXiv 2604.15645 ↗First posted 17 Apr 2026cs.LG physics.comp-ph quant-phcited by 0

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Abstract (arXiv)

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. The framework enables systematic evaluation of PINN performance across benchmark problems including 1D hyperbolic conservation laws, incompressible flows, and electromagnetic wave propagation. It supports a range of architectural and training enhancements, including Fourier feature embeddings, random weight factorization, strict boundary condition enforcement, adaptive loss balancing, curriculum training, and second-order optimization strategies, with extensibility to additional methods. We provide a comprehensive benchmark study quantifying the impact of these methods on convergence, accuracy, and computational cost, and analyze distributed data parallel scaling in terms of runtime and memory efficiency. In addition, we extend the framework to hybrid quantum-classical PINNs and derive a formal estimate for circuit-evaluation complexity under parameter-shift differentiation. Results highlight the sensitivity of PINNs to architectural and training choices, confirm their high computational cost relative to classical solvers, and identify regimes where hybrid quantum models offer improved parameter efficiency. QPINNACLE provides a foundation for benchmarking physics-informed learning methods and guiding future developments through quantitative assessment of their trade-offs.

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References · 31

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  10. 2021Characterizing possible failure modes in physics-informed neural networkscited by 632
  11. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
  12. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 621
19 more
  1. 2019DeepXDE: A deep learning library for solving differential equationscited by 617
  2. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  3. 2021Physics-informed neural networks with hard constraints for inverse designcited by 277
  4. 2022A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networkscited by 233
  5. 2024Challenges in Training PINNs: A Loss Landscape Perspectivecited by 109
  6. 2020A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networkscited by 86
  7. 2023PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEscited by 84
  8. 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 77
  9. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  10. 2024PirateNets: Physics-informed Deep Learning with Residual Adaptive Networkscited by 70
  11. 2021Evolutional Deep Neural Networkcited by 48
  12. 2023A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionscited by 48
  13. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 43
  14. 2022Random Weight Factorization Improves the Training of Continuous Neural Representationscited by 13
  15. 2025Simulating Three-dimensional Turbulence with Physics-informed Neural Networkscited by 13
  16. 2023On the Generalization of PINNs outside the training domain and the Hyperparameters influencing itcited by 10
  17. 2021Nys-Newton: Nystr"om-Approximated Curvature for Stochastic Optimizationcited by 4
  18. 2024Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problemscited by 3
  19. 2025Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Needcited by 2

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