Retrieval of exudate-affected retinal image patches using Siamese quantum classical neural network

Deep neural networks were previously used in the arena of image retrieval. Siamese network architecture is also used for image similarity comparison. Recently, the application of quantum computing in different fields has gained research interest. Researchers are keen to explore the prospect of quantum circuit implementation in terms of supervised learning, resource utilization, and energy-efficient reversible computing. In this study, the authors propose an application of quantum circuit in Siamese architecture and explored its efficiency in the field of exudate-affected retinal image patch retrieval. Quantum computing applied within Siamese network architecture may be effective for image patch characteristic comparison and retrieval work. Although there is a restriction of managing high-dimensional inner product space, the circuit with a limited number of qubits represents exudate-affected retinal image patches and retrieves similar patches from the patch database. Parameterized quantum circuit (PQC) is implemented using a quantum machine learning library on Google Cirq framework. PQC model is composed of classical pre/post-processing and parameterized quantum circuit. System efficiency is evaluated with the most widely used retrieval evaluation metrics: mean average precision (MAP) and mean reciprocal rank (MRR). The system achieved an encouraging and promising result of 98.1336% MAP and 100% MRR. Image pixels are implicitly converted to rectangular grid qubits in this experiment. The experimentation was further extended to IBM Qiskit framework also. In Qiskit, individual pixels are explicitly encoded using novel enhanced quantum representation (NEQR) image encoding algorithm. The probability distributions of both query and database patches are compared through Jeffreys distance to retrieve similar patches.