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Data-Driven Solver
Data-Driven Solver

QA-PINN Delivers 25× Faster CFD Training

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Challenges

Before acceleration, the QA-PINN workflow faced a major compute bottleneck:

  • A 20% reduction in trainable parameters had already been demonstrated for a benchmark CFD problem.
  • However, training on a local T4 GPU took ~85 hours, slowing experimentation.
  • Testing new quantum circuit designs or benchmarking additional PDEs became impractical.

Key limitation: training time, not algorithm capability.

Results

Running QA-PINN on CUDA-Q delivered a substantial performance leap:

  • Training time: 85 hours → 3.5 hours
  • Acceleration: ~25× faster
  • Model quality: Parameter-reduced model-maintained generalization
  • Experimentation bandwidth: 25× more circuit trials possible in the same time

The solver became significantly more practical for real CFD workloads

BQPhy Quantum-Assisted PINNs improves QCFD Training

25X Faster Training

85 hours to 3.5 hours using CUDA-Q on A100 GPU.

Maintained Model Quality
Parameter-reduced model maintains full generalization and accuracy.
Practical Experimentation
25X more circuit trials possible, making complex QCFD workloads viable.
Go Beyond Classical Limits.
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