In order to solve the problem of feature point extraction in quantum images, the implementation scheme of Harris corner detection algorithm on quantum computers is studied. Firstly, to address the efficient encoding and computation of rational numbers in Harris corner detection quantum circuits, a quantum image representation model based on complement floating-point representation is proposed. In this model, pixel values are represented in the form of complement-based floating-point numbers, and the position information of pixels is represented by unsigned numbers. Each data structure is based on the basic state of quantum bit sequences for information representation. Secondly, based on the relevant theories of the classical Harris corner detection algorithm, a specific quantum circuit for Harris corner detection is designed, and its complexity analysis is conducted to verify its exponential acceleration compared to classical schemes. Finally, through simulation experiments on classical computers, it is demonstrated that the overlap rate between the classical Harris corner detection algorithm and the quantum Harris corner detection scheme exceeds 85% , which to some extent verifies the feasibility and effectiveness of the proposed scheme.

quantum image processing, quantum image representation, feature point detection, Harris corner