Multiscale contiguous block pixel entangler for image recognition on hybrid quantum-classical computing system

Abstract

A method of performing implementing a quantum convolutional neural network (QCNN) in a hybrid quantum-classical computing system includes performing a data load operation, a set of a convolutional layer operation and a pooling operation, a measurement operation. The data load operation includes encoding pixel data of an input image onto a quantum processor using column qubits and row qubits. The convolutional layer operation includes a contiguous block pixel entangler that entangles a column qubit and a row qubit, depending on a pattern of a feature to detect in the input image. The pooling layer operation includes applying a series of one-qubit operations to the column qubits and the row qubits. The measurement operation includes measuring a state of an output qubit among the column qubits and the row qubits.

CPC Classifications