Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

TL;DR

This paper introduces a quantum image processing framework that encodes images in quantum states, enabling exponential speed-ups in processing tasks like edge detection, with minimal qubit requirements and single-qubit operations.

Contribution

The paper presents a novel quantum image representation reducing qubit usage and introduces a quantum edge detection algorithm requiring only one qubit operation regardless of image size.

Findings

Quantum image encoding reduces qubit requirements.

Edge detection achieved with a single-qubit operation.

Exponential speed-up over classical methods.

Abstract

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.