Abstract Current era is witnessing rapid growth in data and fast data processing methods. In the past few decades, artificial intelligence has matured exponentially and has been applied in many areas such as medicine, transport, finance, and many more. Classical machine learning methods are resource intensive and usually training models takes time. Quantum machine learning can be a alternate solution to this problem and can be used to solve complex data problems. Quantum machine learning utilizes parallel computing capabilities of quantum computers. Quantum machine learning methods can be used for image classification, which is an important task in data science. Here, we have proposed a quantum convolutional neural network for color image classification, where we have applied a quantum convolutional and pooling layers to reduce the number of features without losing crucial information. We have used the proposed method to classify color image of the CIFAR-10 dataset. We have compared the validation accuracies and loss during training for both classical and quantum convolutional neural network. Our results show that we have achieved similar accuracy with less number of trainable parameters. We believe that this study will open new avenue for high dimensional data classification using quantum convolutional neural network in current and beyond NISQ era.
Hamid et al. (Tue,) studied this question.