  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.
