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Parameterized quantum circuits as machine learning models , volume=

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Correction Crossref 18 open · 18 total · 0 disputed
DOI
10.1088/2058-9565/ab4eb5
Notice DOI
10.1088/2058-9565/ab5944
Event date
2019-12-04
Machine twin
JSON

01One-hop citing occurrences

Correction Open
Single-shot quantum neural networks with amplitude estimation

ref [9] · 2604.19320 · notice #568 · dispute

Raw extraction · citation context

Seo, Efficient quantum machine learning with inverse-probability al- gebraic corrections (2026). arXiv:2601.16665. URLhttps://arxiv.org/abs/2601.16665 [8] M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Science and Technology 4 (4) (2019) 043001. doi:10.1088/2058-9565/ab4eb5. URLhttps://dx.doi.org/10.1088/2058-9565/ab4eb5 [9] T. Hubregtsen, J. Pichlmeier, P. Stecher, K. Bertels, Evaluation of pa- rameterized quantum circuits: on the relation between classification ac- curacy, expressibility, and entangling capability, Quantum Machine In- telligence 3 (1) (2021) 9. doi:10.1007/s42484-021-00038-w. URLhttps://doi.org/10.1007/s42484-021-00038-w [10] J. Shi, W. Wang, X. Lou, S.

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Seo, Efficient quantum machine learning with inverse-probability al- gebraic corrections (2026). arXiv:2601.16665. URLhttps://arxiv.org/abs/2601.16665 [8] M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Science and Technology 4 (4) (2019) 043001. doi:10.1088/2058-9565/ab4eb5. URLhttps://dx.doi.org/10.1088/2058-9565/ab4eb5 [9] T. Hubregtsen, J. Pichlmeier, P. Stecher, K. Bertels, Evaluation of pa- rameterized quantum circuits: on the relation between classification ac- curacy, expressibility, and entangling capability, Quantum Machine In- telligence 3 (1) (2021) 9. doi:10.1007/s42484-021-00038-w. URLhttps://doi.org/10.1007/s42484-021-00038-w [10] J. Shi, W. Wang, X. Lou, S

Correction Open
H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading

ref [3] · 2604.23335 · notice #567 · dispute

Raw extraction · bibliography line

author Benedetti, M. , author Lloyd, E. , author Sack, S. , author Fiorentini, M. , year 2019 . title Parameterized quantum circuits as machine learning models . journal Quantum Science and Technology volume 4 , pages 043001 . :10.1088/2058-9565/ab4eb5

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author Benedetti, M., author Lloyd, E., author Sack, S., author Fiorentini, M., year 2019 . title Parameterized quantum circuits as machine learning models . journal Quantum Science and Technology volume 4, pages 043001 . :10.1088/2058-9565/ab4eb5

Correction Open
Quantum Injection Pathways for Implicit Graph Neural Networks

ref [12] · 2605.09226 · notice #570 · dispute

Raw extraction · citation context

In parallel to these developments in classical implicit mod- eling, quantum machine learning has developed a broad family of hybrid quantum-classical models built on theencode- unitary-measurepattern, in which classical data are encoded into a quantum state by a data-dependent unitary, processed by a parametrized quantum circuit (PQC), and read out through measurements of chosen observables [11], [12]. Applied to graph-structured data, this template has been instantiated as quantum graph kernels, quantum graph neural networks, hy- brid graph classifiers, and graph-generation pipelines [13]- [16]. Recent work has also used graph neural networks to an- alyze parameterized quantum circuits themselves [17]. These models, however, remainexplicit finite-depth architectures.

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In parallel to these developments in classical implicit mod- eling, quantum machine learning has developed a broad family of hybrid quantum-classical models built on theencode- unitary-measurepattern, in which classical data are encoded into a quantum state by a data-dependent unitary, processed by a parametrized quantum circuit (PQC), and read out through measurements of chosen observables [11], [12]. Applied to graph-structured data, this template has been instantiated as quantum graph kernels, quantum graph neural networks, hy- brid graph classifiers, and graph-generation pipelines [13]- [16]. Recent work has also used graph neural networks to an- alyze parameterized quantum circuits themselves [17]. These models, however, remainexplicit finite-depth architectures

Correction Open
Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework

ref [54] · 2606.01291 · notice #579 · dispute

Raw extraction · bibliography line

Parameterized quantum circuits as machine learning models , volume =. Quantum Science and Technology , author =. 2019 , pages =. doi:10.1088/2058-9565/ab4eb5 , abstract =

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Parameterized quantum circuits as machine learning models, volume =. Quantum Science and Technology, author =. 2019, pages =. doi:10.1088/2058-9565/ab4eb5, abstract =

Correction Open
Benchmark of Pauli Correlation Encoding for different optimisation problems

ref [13] · 2606.18914 · notice #576 · dispute

Raw extraction · bibliography line

Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia , year=. Parameterized quantum circuits as machine learning models , volume=. Quantum Science and Technology , publisher=. doi:10.1088/2058-9565/ab4eb5 , number=

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Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia, year=. Parameterized quantum circuits as machine learning models, volume=. Quantum Science and Technology, publisher=. doi:10.1088/2058-9565/ab4eb5, number=

Correction Open
Benchmark of Pauli Correlation Encoding for different optimisation problems

ref [13] · 2606.18914 · notice #584 · dispute

Raw extraction · bibliography line

Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia , year=. Parameterized quantum circuits as machine learning models , volume=. Quantum Science and Technology , publisher=. doi:10.1088/2058-9565/ab4eb5 , number=

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Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia, year=. Parameterized quantum circuits as machine learning models, volume=. Quantum Science and Technology, publisher=. doi:10.1088/2058-9565/ab4eb5, number=

Correction Open
Benchmark of Pauli Correlation Encoding for different optimisation problems

ref [13] · 2606.18914 · notice #582 · dispute

Raw extraction · bibliography line

Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia , year=. Parameterized quantum circuits as machine learning models , volume=. Quantum Science and Technology , publisher=. doi:10.1088/2058-9565/ab4eb5 , number=

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Benedetti, Marcello and Lloyd, Erika and Sack, Stefan and Fiorentini, Mattia, year=. Parameterized quantum circuits as machine learning models, volume=. Quantum Science and Technology, publisher=. doi:10.1088/2058-9565/ab4eb5, number=

Correction Open
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder

ref [10] · 2606.27411 · notice #580 · dispute

Raw extraction · bibliography line

Benedetti, M., Lloyd, E., Sack, S., & Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology , 4(4), 043001. https://doi.org/10.1088/2058-9565/ab4eb5

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Benedetti, M., Lloyd, E., Sack, S., & Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4), 043001. https://doi.org/10.1088/2058-9565/ab4eb5

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