Quantum Optics based Algorithm for Measuring the Similarity between Images

TL;DR

This paper introduces a quantum optics-based algorithm for image similarity measurement, utilizing coherent states and quantum superposition, with potential applications in image retrieval and machine learning.

Contribution

The paper presents a novel quantum optics formulation for image similarity measurement, encoding multiple images via quantum superposition, and demonstrates its effectiveness through noise robustness and quality assessment.

Findings

Good agreement with expected image quality outcomes

Effective encoding of multiple images using quantum superposition

Sensitivity analysis shows robustness to image distortions

Abstract

We report an algorithm, based on quantum optics formulation, where a coherent state is used as the elementary quantum resource for the image representation. We provide an architecture with constituent optical elements in linear order with respect to the image resolution. The obtained phase-distributed multimode coherent state is fed into an image retrieval scheme and we identify the appropriate laser intensity parameter for similarity measurement. The use of the principle of quantum superposition in the similarity measurement protocol enables us to encode multiple input images. We demonstrate the viability of the protocol through an objective quality assessment of images by adding consecutive layers of noises. The results are in good agreement with the expected outcome. The image distortion-sensitivity analysis of the metric establishes the further merit of the model. Our quantum algorithm has wider applicability also in supervised machine learning tasks.