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

arXiv: https://arxiv.org/abs/2110.15672 · HTML: https://mlsanity.com/paper/2110.15672/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era](https://mlsanity.com/paper/2102.05321/) (2021)

## Built on by (most cited first, 6 of 6)

- [A hybrid quantum image edge detector for the NISQ era](https://mlsanity.com/paper/2203.12072/) (2022)
- [Hybrid quantum transfer learning for crack image classification on NISQ hardware](https://mlsanity.com/paper/2307.16723/) (2023)
- [Schr"odinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera](https://mlsanity.com/paper/2303.07510/) (2023)
- [Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images](https://mlsanity.com/paper/2603.28995/) (2026)
- [Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels](https://mlsanity.com/paper/2605.00744/) (2026)
- [Schmidt Decomposition-Based Methods for Efficient Quantum Image Encoding](https://mlsanity.com/paper/2606.10874/) (2026)

## Abstract (arXiv)

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.