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Results

Data Driven solver

Discover How BQP's Quantum Assisted-PINN Algorithm Cuts Training Time, Cost, and Complexity

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Challenges

Machine Learning for solving PDEs is limited by:
- Generalizability: Testing for multiple conditions for the same geometry without retraining the entire model)
- Training efficiency for simulating transient, incompressible, viscous, non-linear flows

Results

The data-driven solver addressed complex fluid flow PDEs by enhancing a classical Physics Informed Neural Network (PINN) with quantum layers.
Each QA-PINN (2, 3, and 5-qubit) uses Quantum gate layers with alternating full entanglement, combining quantum and classical hidden layers, with input (x and t) and output (u) layers.

QA-PINN Outperforms Classical PINN

20%

Trainable parameter reduction

‍

98.04%

Accuracy

Enhances Generalizability

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