A hybrid quantum image edge detector for the NISQ era
This work addresses the computational bottleneck in image processing for applications like computer vision by leveraging quantum computing, though it is incremental as it builds on existing quantum techniques.
The authors tackled the problem of edge detection in large images by proposing a hybrid quantum method based on quantum artificial neurons, which can be implemented on current noisy quantum computers and scales to handle images larger than previously possible.
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.