A hybrid quantum image edge detector for the NISQ era

TL;DR

This paper introduces a hybrid quantum edge detection method suitable for current noisy quantum computers, enabling scalable image processing with reduced circuit complexity and improved ability to handle larger images.

Contribution

It presents a novel hybrid quantum edge detection technique based on quantum artificial neurons, optimized for NISQ-era quantum hardware, and demonstrates scalability for larger images.

Findings

Six variants reduce circuit complexity and processing time.

Method enables edge detection on larger images than previously possible.

Practical implementation on current quantum hardware demonstrated.

Abstract

Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved for example using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.