Cargando…
Quantum pixel representations and compression for N-dimensional images
We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and si...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095730/ https://www.ncbi.nlm.nih.gov/pubmed/35546151 http://dx.doi.org/10.1038/s41598-022-11024-y |
_version_ | 1784705821460922368 |
---|---|
author | Amankwah, Mercy G. Camps, Daan Bethel, E. Wes Van Beeumen, Roel Perciano, Talita |
author_facet | Amankwah, Mercy G. Camps, Daan Bethel, E. Wes Van Beeumen, Roel Perciano, Talita |
author_sort | Amankwah, Mercy G. |
collection | PubMed |
description | We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of [Formula: see text] gates and [Formula: see text] gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library. |
format | Online Article Text |
id | pubmed-9095730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90957302022-05-13 Quantum pixel representations and compression for N-dimensional images Amankwah, Mercy G. Camps, Daan Bethel, E. Wes Van Beeumen, Roel Perciano, Talita Sci Rep Article We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of [Formula: see text] gates and [Formula: see text] gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library. Nature Publishing Group UK 2022-05-11 /pmc/articles/PMC9095730/ /pubmed/35546151 http://dx.doi.org/10.1038/s41598-022-11024-y Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Amankwah, Mercy G. Camps, Daan Bethel, E. Wes Van Beeumen, Roel Perciano, Talita Quantum pixel representations and compression for N-dimensional images |
title | Quantum pixel representations and compression for N-dimensional images |
title_full | Quantum pixel representations and compression for N-dimensional images |
title_fullStr | Quantum pixel representations and compression for N-dimensional images |
title_full_unstemmed | Quantum pixel representations and compression for N-dimensional images |
title_short | Quantum pixel representations and compression for N-dimensional images |
title_sort | quantum pixel representations and compression for n-dimensional images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095730/ https://www.ncbi.nlm.nih.gov/pubmed/35546151 http://dx.doi.org/10.1038/s41598-022-11024-y |
work_keys_str_mv | AT amankwahmercyg quantumpixelrepresentationsandcompressionforndimensionalimages AT campsdaan quantumpixelrepresentationsandcompressionforndimensionalimages AT bethelewes quantumpixelrepresentationsandcompressionforndimensionalimages AT vanbeeumenroel quantumpixelrepresentationsandcompressionforndimensionalimages AT percianotalita quantumpixelrepresentationsandcompressionforndimensionalimages |