Sample images of FruitQ dataset.

<div><p>This study investigates hybrid quantum neural networks for fruit quality assessment, with a focus on the impact of the entangling gate choice. Two architectures were developed: NNQEv1, utilizing controlled-NOT (CNOT) gates, and NNQEv2, employing controlled-phase (CZ) gates. A the...

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主要作者:	Danish ul Khairi (19660534) (author)
其他作者:	Kamran Ahsan (10413151) (author), Syed Zeeshan Ali (19660531) (author), Wadee Alhalabi (11951405) (author), Somayah Albaradei (9041843) (author), Muhammad Shahid Anwar (19660537) (author)
出版:	2025
主題:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified level design choices fruit quality assessment experimental results align aware noise considerations achieving test accuracies scarce apple dataset stable training dynamics entangling gate choice based nnqev2 model fruitq dataset xlink "> work presents utilizing controlled two architectures theoretical justification theoretical analysis quantum circuits gate decomposition employing controlled consistently showed computational study computational execution based architecture
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Sample images of FruitQ dataset.

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