Design of quantum filter for hybrid quantum-classical convolutional neural networks

Convolutional neural networks are widely used in image recognition problems as they have many advantages compared to other techniques. Parametrized quantum circuit, at the same time, is widely used for supervised learning task like images classification. Here we design two quantum filters for hybrid quantum-classical convolutional neural network for image recognition tasks using parametrized quantum circuit. We demonstrate the potential of these quantum filters by applying them to UCI digits dataset, and show that the hybrid quantum-classical convolutional neural network can accomplish classification tasks with a high learning accuracy. To explore how the quantum filter enhances the feature mapping process, we carry out a series of simulation experiments on different parametrized quantum circuit structure, quantum filter depth, and tasks of varying difficulty. The proposed quantum filters achieve excellent classification accuracy under a limited number of qubits available in current hardware, indicating the promising application potential of it in image recognition.