ATTENTION: The software behind KU ScholarWorks is being upgraded to a new version. Starting July 15th, users will not be able to log in to the system, add items, nor make any changes until the new version is in place at the end of July. Searching for articles and opening files will continue to work while the system is being updated.
If you have any questions, please contact Marianne Reed at mreed@ku.edu .
Generalized Quantum Convolution for Multidimensional Data
dc.contributor.author | Jeng, Mingyoung | |
dc.contributor.author | Nobel, Alvir | |
dc.contributor.author | Jha, Vinayak | |
dc.contributor.author | Levy, David | |
dc.contributor.author | Kneidel, Dylan | |
dc.contributor.author | Chaudhary, Manu | |
dc.contributor.author | Islam, Ishraq | |
dc.contributor.author | Rahman, Muhammad Momin | |
dc.contributor.author | El-Araby, Esam | |
dc.date.accessioned | 2024-06-11T18:04:48Z | |
dc.date.available | 2024-06-11T18:04:48Z | |
dc.date.issued | 2023-10-31 | |
dc.identifier.citation | Jeng M, Nobel A, Jha V, Levy D, Kneidel D, Chaudhary M, Islam I, Rahman MM, El-Araby E. Generalized Quantum Convolution for Multidimensional Data. Entropy (Basel). 2023 Oct 31;25(11):1503. doi: 10.3390/e25111503. PMID: 37998195; PMCID: PMC10670423 | en_US |
dc.identifier.uri | https://hdl.handle.net/1808/35122 | |
dc.description.abstract | The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum. | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Convolution | en_US |
dc.subject | Quantum algorithms | en_US |
dc.subject | Quantum image processing | en_US |
dc.subject | Quantum computing | en_US |
dc.title | Generalized Quantum Convolution for Multidimensional Data | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Jeng, Mingyoung | |
kusw.kuauthor | Nobel, Alvir | |
kusw.kuauthor | Jha, Vinayak | |
kusw.kuauthor | Levy, David | |
kusw.kuauthor | Kneidel, Dylan | |
kusw.kuauthor | Chaudhary, Manu | |
kusw.kuauthor | Islam, Ishraq | |
kusw.kuauthor | Rahman, Muhammad Momin | |
kusw.kuauthor | El-Araby, Esam | |
kusw.kudepartment | Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.doi | https://doi.org/10.3390%2Fe25111503 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-5440-923X | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-4575-1049 | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC10670423 | en_US |
dc.rights.accessrights | openAccess | en_US |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's license is described as: Copyright © 2023 by the authors.
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).