The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Unsupervised Generative Modeling Using Matrix Product States
Open accessAbstract
Modeling the probability distribution of complex data using insights from quantum physics is a fresh approach to generative modeling in machine learning, and shows great potential compared to conventional neural network approaches.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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Domain-Adversarial Training of Neural Networks
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Cited by
A generative modeling approach for benchmarking and training shallow quantum circuits
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., & Perdomo-Ortiz, A. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0157-8 · Cited by 379
Towards quantum machine learning with tensor networks
Huggins, W., Patil, P., Mitchell, B., Whaley, K. B., & Stoudenmire, E. M. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/aaea94 · Cited by 366
Differentiable learning of quantum circuit Born machines
Liu, J.-G., & Wang, L. (2018) · Physical Review A · doi:10.1103/physreva.98.062324 · Cited by 279
Self-citationDifferentiable Programming Tensor Networks
Liao, H.-J., Liu, J.-G., Wang, L., & Xiang, T. (2019) · Physical Review X · doi:10.1103/physrevx.9.031041 · Cited by 251
Self-citationGenerative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning
Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024) · Acta Informatica Pragensia · doi:10.18267/j.aip.235 · Cited by 218
Metadata
- Authors
- Han, Z.-Y., Wang, J., Fan, H., Wang, L., & Zhang, P.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-07-17
- Volume
- 8
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2753545915
- Cited by
- 256
- References
- 63
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- gold
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- Han, Z.-Y., Wang, J., Fan, H., Wang, L., & Zhang, P. (2018). Unsupervised Generative Modeling Using Matrix Product States. Physical Review X. https://doi.org/10.1103/physrevx.8.031012
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
