Quantum pixel representations and compression for $N$-dimensional images

TL;DR

This paper introduces QPIXL, a unified quantum pixel representation framework that improves circuit efficiency and reduces complexity, enabling practical quantum image processing and compression on NISQ devices.

Contribution

The paper presents a universal quantum pixel framework that simplifies circuit design, reduces gate complexity, and includes a highly effective image compression algorithm.

Findings

Reduces gate complexity by up to 90% for scientific images

Uses only Ry and CNOT gates, no ancilla qubits needed

Achieves efficient quantum image representation suitable for NISQ devices

Abstract

We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method only requires a linear number of gates in terms of the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of Ry gates and CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library.