# A novel state connection strategy for quantum computing to represent and compress digital images

arXiv: https://arxiv.org/abs/2212.07079 · HTML: https://mlsanity.com/paper/2212.07079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Faster than Classical Quantum Algorithm for dense Formulas of Exact Satisfiability and Occupation Problems](https://mlsanity.com/paper/1512.00859/) (2015)
- [Advance quantum image representation and compression using DCTEFRQI approach](https://mlsanity.com/paper/2208.14277/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient quantum image representation and compression circuit using zero-discarded state preparation approach](https://mlsanity.com/paper/2306.12634/) (2023)

## Abstract (arXiv)

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.