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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2025
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| _version_ | 1851480305996660736 |
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| author | Danish ul Khairi (19660534) |
| author2 | Kamran Ahsan (10413151) Syed Zeeshan Ali (19660531) Wadee Alhalabi (11951405) Somayah Albaradei (9041843) Muhammad Shahid Anwar (19660537) |
| author2_role | author author author author author |
| author_facet | Danish ul Khairi (19660534) Kamran Ahsan (10413151) Syed Zeeshan Ali (19660531) Wadee Alhalabi (11951405) Somayah Albaradei (9041843) Muhammad Shahid Anwar (19660537) |
| author_role | author |
| dc.creator.none.fl_str_mv | Danish ul Khairi (19660534) Kamran Ahsan (10413151) Syed Zeeshan Ali (19660531) Wadee Alhalabi (11951405) Somayah Albaradei (9041843) Muhammad Shahid Anwar (19660537) |
| dc.date.none.fl_str_mv | 2025-12-10T18:30:45Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332528.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Sample_images_of_FruitQ_dataset_/30852093 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Sample images of FruitQ dataset. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <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 theoretical justification is provided, based on gate decomposition and hardware-aware noise considerations, suggesting the CZ-based architecture is likely to be more stable. The performance of the models was evaluated through the computational execution of their quantum circuits on classical hardware and compared against classical and state-of-the-art deep learning models. The proposed models demonstrated competitive performance, achieving test accuracies of 98.7% on MNIST, 98.6% on the FruitQ dataset, and 96.7% on a custom, data-scarce Apple dataset. The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. This work presents a foundational, computational study on the role of gate-level design choices, intended to inform the development of future quantum machine learning algorithms.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_ada60d0e6918a5429f0c24d09f3de636 |
| identifier_str_mv | 10.1371/journal.pone.0332528.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30852093 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sample images of FruitQ dataset.Danish ul Khairi (19660534)Kamran Ahsan (10413151)Syed Zeeshan Ali (19660531)Wadee Alhalabi (11951405)Somayah Albaradei (9041843)Muhammad Shahid Anwar (19660537)Biological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedlevel design choicesfruit quality assessmentexperimental results alignaware noise considerationsachieving test accuraciesscarce apple datasetstable training dynamicsentangling gate choicebased nnqev2 modelfruitq datasetxlink ">work presentsutilizing controlledtwo architecturestheoretical justificationtheoretical analysisquantum circuitsgate decompositionemploying controlledconsistently showedcomputational studycomputational executionbased architecture<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 theoretical justification is provided, based on gate decomposition and hardware-aware noise considerations, suggesting the CZ-based architecture is likely to be more stable. The performance of the models was evaluated through the computational execution of their quantum circuits on classical hardware and compared against classical and state-of-the-art deep learning models. The proposed models demonstrated competitive performance, achieving test accuracies of 98.7% on MNIST, 98.6% on the FruitQ dataset, and 96.7% on a custom, data-scarce Apple dataset. The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. This work presents a foundational, computational study on the role of gate-level design choices, intended to inform the development of future quantum machine learning algorithms.</p></div>2025-12-10T18:30:45ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332528.g005https://figshare.com/articles/figure/Sample_images_of_FruitQ_dataset_/30852093CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308520932025-12-10T18:30:45Z |
| spellingShingle | Sample images of FruitQ dataset. Danish ul Khairi (19660534) 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 |
| status_str | publishedVersion |
| title | Sample images of FruitQ dataset. |
| title_full | Sample images of FruitQ dataset. |
| title_fullStr | Sample images of FruitQ dataset. |
| title_full_unstemmed | Sample images of FruitQ dataset. |
| title_short | Sample images of FruitQ dataset. |
| title_sort | Sample images of FruitQ dataset. |
| topic | 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 |