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Data Driven Solver
Data Driven Solver

Quantum-Inspired Crack Detection Achieving Near-Perfect Accuracy

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Challenges
  • Traditional methods struggle with accurate crack detection in infrastructure due to complex patterns, varying lighting, shadows, and textures in images.

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Results

  • BQPhy’s hybrid quantum-classical  convolutional neural network (HQCNN) leveraging quantum computing principles  enhances image classification and accuracy, especially with imbalanced or limited data.
  • HQCNN outperformed the classical model in accuracy (up to 100%) and F1-score, on imbalanced and small datasets, while using far fewer trainable parameters, leading to more efficient and reliable crack detection.

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BQPhy’s HQCNN outperforms CCNN

Accuracy reaches 100%

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Less training time
Supports unbalanced dataset
Go Beyond Classical Limits.
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