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 and examine its performance on a real defect detection application in one of the components of the ultrasonic sensors produced at Robert Bosch's manufacturing plants. Our quantum-inspired T-CNN operates on a reduced model parameter space to substantially improve the training speed and performance of an equivalent CNN model without sacrificing accuracy. More specifically%2C we demonstrate how T-CNNs are able to reach the same performance as classical CNNs as measured by quality metrics%2C with up to fifteen times fewer parameters and 4%25 to 19%25 faster training times. Our results demonstrate that the T-CNN greatly outperforms the results of traditional human visual inspection%2C providing value in a current real application in manufacturing.?quality=80&w=800)
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