 Hybrid quantum-classical convolutional networks for robust denoising of quantum images in noisy systemsOla Al-Ta’ani () International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 1607-1618 Abstract: Quantum imaging systems produce images with distinctive noise patterns that conventional denoising algorithms cannot effectively process. We present an innovative neural network architecture that merges quantum physics principles with deep learning to address this challenge. Our hybrid approach adapts standard image processing techniques to handle quantum-specific noise while preserving critical image features. Experimental validation demonstrates a consistent 12.6% improvement in output quality compared to existing methods, with efficient performance on standard computing hardware. Additionally, the model exhibits strong generalization capabilities, achieving robust performance across varying noise levels. This advancement represents an important step toward practical quantum imaging applications in fields ranging from medical diagnostics to secure communications. Keywords: Convolutional neural networks; deep learning; hybrid quantum-classical systems; noisy quantum systems; quantum image denoising; quantum image processing. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:3:p:1607-1618:id:6842 Access Statistics for this article International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing |
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