Improved FRQI on superconducting processors and its restrictions in the NISQ era

TL;DR

This paper explores the practical implementation of the FRQI quantum image encoding method on current noisy quantum processors, proposing circuit simplifications to handle larger images within NISQ limitations.

Contribution

It introduces a simplified circuit approach for FRQI, enabling larger image encoding on NISQ devices and assesses practical feasibility on real quantum hardware.

Findings

FRQI can be implemented on current quantum hardware for small images

Circuit simplifications reduce gate count and error accumulation

Larger images can be encoded with optimized circuits on NISQ devices

Abstract

In image processing, the amount of data to be processed grows rapidly, in particular when imaging methods yield images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the Flexible Representation of Quantum Images (FRQI). Furthermore, we check experimentally what is the limit in the current noisy intermediate-scale quantum era, i.e. up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates needed, especially of the error-prone controlled-NOT gates, can be reduced. As a consequence, the size of manageable images increases.