Sample images of FruitQ dataset.

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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Dettagli Bibliografici
Autore principale: Danish ul Khairi (19660534) (author)
Altri autori: Kamran Ahsan (10413151) (author), Syed Zeeshan Ali (19660531) (author), Wadee Alhalabi (11951405) (author), Somayah Albaradei (9041843) (author), Muhammad Shahid Anwar (19660537) (author)
Pubblicazione: 2025
Soggetti:
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
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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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