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Results

Data Driven Solver

Discover how Quantum-Inspired Crack Detection with BQPhy® Achieves 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

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