Generalized Quantum Convolution for Multidimensional Data
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/11/1503 |
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| author | Mingyoung Jeng Alvir Nobel Vinayak Jha David Levy Dylan Kneidel Manu Chaudhary Ishraq Islam Muhammad Momin Rahman Esam El-Araby |
| author_facet | Mingyoung Jeng Alvir Nobel Vinayak Jha David Levy Dylan Kneidel Manu Chaudhary Ishraq Islam Muhammad Momin Rahman Esam El-Araby |
| author_sort | Mingyoung Jeng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:51:13Z |
| format | Article |
| id | doaj.art-ca7075b83f6349dda8a5e5a67a375317 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T16:51:13Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-ca7075b83f6349dda8a5e5a67a3753172023-11-24T14:40:56ZengMDPI AGEntropy1099-43002023-10-012511150310.3390/e25111503Generalized Quantum Convolution for Multidimensional DataMingyoung Jeng0Alvir Nobel1Vinayak Jha2David Levy3Dylan Kneidel4Manu Chaudhary5Ishraq Islam6Muhammad Momin Rahman7Esam El-Araby8Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USADepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USAThe 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.https://www.mdpi.com/1099-4300/25/11/1503convolutionquantum algorithmsquantum image processingquantum computing |
| spellingShingle | Mingyoung Jeng Alvir Nobel Vinayak Jha David Levy Dylan Kneidel Manu Chaudhary Ishraq Islam Muhammad Momin Rahman Esam El-Araby Generalized Quantum Convolution for Multidimensional Data Entropy convolution quantum algorithms quantum image processing quantum computing |
| title | Generalized Quantum Convolution for Multidimensional Data |
| title_full | Generalized Quantum Convolution for Multidimensional Data |
| title_fullStr | Generalized Quantum Convolution for Multidimensional Data |
| title_full_unstemmed | Generalized Quantum Convolution for Multidimensional Data |
| title_short | Generalized Quantum Convolution for Multidimensional Data |
| title_sort | generalized quantum convolution for multidimensional data |
| topic | convolution quantum algorithms quantum image processing quantum computing |
| url | https://www.mdpi.com/1099-4300/25/11/1503 |
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