ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks
<p dir="ltr">The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by cla...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513510664306688 |
|---|---|
| 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 | 2024-01-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1140/epjqt/s40507-023-00216-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ResQNets_a_residual_approach_for_mitigating_barren_plateaus_in_quantum_neural_networks/26325040 |
| 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 Machine learning Theory of computation Quantum neural networks Barren plateaus Parameterized quantum circuits Residual learning |
| dc.title.none.fl_str_mv | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.</p><h2>Other Information</h2><p dir="ltr">Published in: EPJ Quantum Technology<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1140/epjqt/s40507-023-00216-8" target="_blank">https://dx.doi.org/10.1140/epjqt/s40507-023-00216-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1ca5787b18a624b7b57be0f484f28e7d |
| identifier_str_mv | 10.1140/epjqt/s40507-023-00216-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26325040 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networksMuhammad Kashif (3923483)Saif Al-Kuwari (16904610)Information and computing sciencesMachine learningTheory of computationQuantum neural networksBarren plateausParameterized quantum circuitsResidual learning<p dir="ltr">The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.</p><h2>Other Information</h2><p dir="ltr">Published in: EPJ Quantum Technology<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1140/epjqt/s40507-023-00216-8" target="_blank">https://dx.doi.org/10.1140/epjqt/s40507-023-00216-8</a></p>2024-01-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1140/epjqt/s40507-023-00216-8https://figshare.com/articles/journal_contribution/ResQNets_a_residual_approach_for_mitigating_barren_plateaus_in_quantum_neural_networks/26325040CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263250402024-01-10T09:00:00Z |
| spellingShingle | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks Muhammad Kashif (3923483) Information and computing sciences Machine learning Theory of computation Quantum neural networks Barren plateaus Parameterized quantum circuits Residual learning |
| status_str | publishedVersion |
| title | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| title_full | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| title_fullStr | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| title_full_unstemmed | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| title_short | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| title_sort | ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks |
| topic | Information and computing sciences Machine learning Theory of computation Quantum neural networks Barren plateaus Parameterized quantum circuits Residual learning |