The DOI matches its Crossref record; 56 of the 63 references it cites were found in a registry or an index.
Quantum Entanglement in Deep Learning Architectures
Open accessAbstract
Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more traditional architectures such as restricted Boltzmann machines (RBMs) and fully connected neural networks. In this Letter, we establish that contemporary deep learning architectures, in the form of deep convolutional and recurrent networks, can efficiently represent highly entangled quantum systems. By constructing tensor network equivalents of these architectures, we identify an inherent reuse of information in the network operation as a key trait which distinguishes them from standard tensor network-based representations, and which enhances their entanglement capacity. Our results show that such architectures can support volume-law entanglement scaling, polynomially more efficiently than presently employed RBMs. Thus, beyond a quantification of the entanglement capacity of leading deep learning architectures, our analysis formally motivates a shift of trending neural-network-based wave function representations closer to the state-of-the-art in machine learning.
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“Tensor networks can encode long-range dependencies, nonlinear relationships, and the integration of distributed features into unified percepts—core challenges that classical models tend to address only through ad hoc architectural modifications.”
From Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints (2026) · cites this paper as [34]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
(2011) · Cited by 6,993
Research works printed but not linked: 10
- D. Amodei, 2016, Proceedings of the International Conference on Machine Learning
- N. Cohen, 2016, Proceedings of the Conference On Learning Theory (COLT)
- Y. Levine, 2018, Proceedings of the 6th International Conference on Learning Representations (ICLR)
- X. Glorot, 2011, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics
- C. Szegedy, 2016, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
- N. Cohen, 2016, Proceedings of the International Conference on Machine Learning (ICML)
- N. Cohen, 2017, Proceedings of the 5th International Conference on Learning Representations (ICLR)
- N. Cohen, 2016, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- V. Khrulkov, 2018, Proceedings of the 6th International Conference on Learning Representations (ICLR)
- O. Sharir, 2018, Proceedings of the 6th International Conference on Learning Representations (ICLR)
Cited by
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Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517
Tensor networks for complex quantum systems
Orús, R. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0086-7 · Cited by 734
Recurrent neural network wave functions
Hibat-Allah, M., Ganahl, M., Hayward, L. E., Melko, R. G., & Carrasquilla, J. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023358 · Cited by 274
Hierarchical quantum classifiers
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V, Green, A. G., & Simone Severini, B. (2018) · UCL Discovery (University College London) · Cited by 296
Metadata
- Authors
- Levine, Y., Sharir, O., Cohen, N., & Shashua, A.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Institute for Advanced Study (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2019-02-12
- Volume
- 122
- Issue
- 6
- Pages
- 065301-065301
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2912516940
- Cited by
- 206
- References
- 63
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 22 · 2023: 27 · 2022: 32 · 2021: 39 · 2020: 33 · 2019: 10
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Israel Science Foundation (1790/12)
- H2020 European Research Council
- Israel Academy of Sciences and Humanities
- Schmidt Foundation
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum entanglement, deep convolutional neural networks, recurrent neural networks, tensor networks, wave function representation, restricted Boltzmann machine, entanglement capacity, information reuse
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