Quantum convolutional neural network circuit

A quantum information processing (QIP) system for classifying an object in an image using a quantum convolutional neural network (QCNN), comprising:

• at least one memory;
• and
• at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to:
• receive the image depicting the object;
• compress, using an autoencoder, the image by extracting a plurality of features;
• encode the plurality of features into quantum states using a quantum feature map that corresponds to a quantum encoding circuit unique to the image, wherein parameters of the quantum encoding circuit depend on pixel values in the image;
• input the encoded plurality of features into the QCNN that is executed on a parameterized quantum circuit (PQC) and trained to detect the object and generate a classification of the object from a plurality of classes, wherein the QCNN comprises a plurality of layers including one or more convolutional layers that apply a unitary operator to neighboring qubits in the one or more convolutional layers;
• and
• output the classification of the object generated by the QCNN.