The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs
<p dir="ltr">Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challen...
محفوظ في:
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| مؤلفون آخرون: | |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513527919673344 |
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| author | Muhammad Kashif (3923483) |
| author2 | Saif Al-Kuwari (16904610) |
| author2_role | author |
| author_facet | Muhammad Kashif (3923483) Saif Al-Kuwari (16904610) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Kashif (3923483) Saif Al-Kuwari (16904610) |
| dc.date.none.fl_str_mv | 2023-07-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1080/17445760.2023.2231163 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_unified_effect_of_data_encoding_ansatz_expressibility_and_entanglement_on_the_trainability_of_HQNNs/25187612 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Quantum machine learning entanglement data encoding quantum neural networks trainability |
| dc.title.none.fl_str_mv | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Parallel, Emergent and Distributed Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/17445760.2023.2231163" target="_blank">https://dx.doi.org/10.1080/17445760.2023.2231163</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_30c1a798a23246b9f359bc7dd8d565be |
| identifier_str_mv | 10.1080/17445760.2023.2231163 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25187612 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNsMuhammad Kashif (3923483)Saif Al-Kuwari (16904610)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningQuantum machine learningentanglementdata encodingquantum neural networkstrainability<p dir="ltr">Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Parallel, Emergent and Distributed Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/17445760.2023.2231163" target="_blank">https://dx.doi.org/10.1080/17445760.2023.2231163</a></p>2023-07-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1080/17445760.2023.2231163https://figshare.com/articles/journal_contribution/The_unified_effect_of_data_encoding_ansatz_expressibility_and_entanglement_on_the_trainability_of_HQNNs/25187612CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/251876122023-07-17T03:00:00Z |
| spellingShingle | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs Muhammad Kashif (3923483) Information and computing sciences Artificial intelligence Data management and data science Machine learning Quantum machine learning entanglement data encoding quantum neural networks trainability |
| status_str | publishedVersion |
| title | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| title_full | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| title_fullStr | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| title_full_unstemmed | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| title_short | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| title_sort | The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Quantum machine learning entanglement data encoding quantum neural networks trainability |