GEQIE Framework for Rapid Quantum Image Encoding

TL;DR

The paper introduces the GEQIE Python framework for efficient quantum image encoding, enabling rapid state creation, benchmarking under noise, and application to complex multidimensional data like cosmic web images.

Contribution

It presents a novel, extendable framework for quantum image encoding that supports benchmarking and complex data applications, advancing quantum image processing research.

Findings

Correctness of encoding methods demonstrated through noise benchmarking

Framework supports high-accuracy retrieval of complex data

Showcase of encoding cosmic web dark-matter density snapshot

Abstract

This work presents a Python framework named after the General Equation of Quantum Image Encoding (GEQIE). The framework creates the image-encoding state using a unitary gate, which can later be transpiled to target quantum backends. The benchmarking results, simulated with different noise levels, demonstrate the correctness of the already implemented encoding methods and the usability of the framework for more sophisticated research tasks based on quantum image encodings. Additionally, we present a showcase example of Cosmic Web dark-matter density snapshot encoding and high-accuracy retrieval (PCC = 0.995) to demonstrate the extendability of the GEQIE framework to multidimensional data and its applicability to other fields of research.