Quantum image processing on real superconducting and trapped-ion based quantum computers

The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 x 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme's quantum part is an artificial neuron, working well on real quantum computers, too.