Defect detection is one of the most important yet challenging tasks in the
quality control stage in the manufacturing sector. In this work, we introduce a
Tensor Convolutional Neural Network (T-CNN) 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, we demonstrate how T-CNNs are able to reach the
same performance as classical CNNs as measured by quality metrics, with up to
fifteen times fewer parameters and 4% to 19% faster training times. Our results
demonstrate that the T-CNN greatly outperforms the results of traditional human
visual inspection, providing value in a current real application in
manufacturing.

在制造业质量控制阶段，缺陷检测是最重要且具有挑战性的任务之一。本文引入了一种张量卷积神经网络（T-CNN），并在罗伯特・博世公司生产的超声波传感器组件的实际缺陷检测应用中对其性能进行了研究。我们的量子启发式 T-CNN 在减少模型参数空间的基础上操作，从而大幅提高了相当于 CNN 模型的训练速度和性能，同时又不损失准确性。具体而言，我们展示了 T-CNN 如何以质量评价指标衡量的同等性能，在参数数量减少了 15 倍的情况下，训练时间加快了 4% 至 19%。我们的结果表明，T-CNN 大大优于传统人工视觉检测的结果，在制造业的实际应用中具有重要价值。

利用张量卷积神经网络提高制造业中的缺陷检测

Boosting Defect Detection in Manufacturing using Tensor Convolutional  Neural Networks

Generative models are a class of machine learning models that aim to learn
the underlying probability distribution of data. Unlike discriminative models,
generative models focus on capturing the data's inherent structure, allowing
them to generate new samples that resemble the original data. To fully exploit
the potential of modeling probability distributions using quantum physics, a
quantum-inspired generative model known as the Born machines have shown great
advancements in learning classical and quantum data over matrix product
state(MPS) framework. The Born machines support tractable log-likelihood,
autoregressive and mask sampling, and have shown outstanding performance in
various unsupervised learning tasks. However, much of the current research has
been centered on improving the expressive power of MPS, predominantly embedding
each token directly by a corresponding tensor index. In this study, we
generalize the embedding method into trainable quantum measurement operators
that can be simultaneously honed with MPS. Our study indicated that combined
with trainable embedding, Born machines can exhibit better performance and
learn deeper correlations from the dataset.

使用可训练的量子测量算子将嵌入方法推广到 Born 机器，研究发现结合可训练的嵌入，Born 机器可以表现出更好的性能，并从数据集中学习更深层次的相关性。