The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Open accessAbstract
Two tools show great promise in approximating low-temperature, condensed-matter systems: Tensor-network states and artificial neural networks. A new analysis builds a bridge between these techniques, opening the way to a host of powerful approaches to understanding complex quantum systems.
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Cites
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Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
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White, S. R. (1992) · Physical Review Letters · doi:10.1103/physrevlett.69.2863 · Cited by 7,702
The density-matrix renormalization group in the age of matrix product states
Schollwöck, U. (2011) · Annals of Physics · doi:10.1016/j.aop.2010.09.012 · Cited by 4,818
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Self-citationA learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
Machine learning phases of matter
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Computational complexity of projected entangled pair states
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Self-citationQuantum gate learning in qubit networks: Toffoli gate without time-dependent control
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Self-citation
Cited by
Discovering Physical Concepts with Neural Networks
Iten, R., Metger, T., Wilming, H., del Rio, L., & Renner, R. (2020) · Physical Review Letters · doi:10.1103/physrevlett.124.010508 · Cited by 458
Expressive power of parametrized quantum circuits
Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033125 · Cited by 318
Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems
Sharir, O., Levine, Y., Wies, N., Carleo, G., & Shashua, A. (2020) · Physical Review Letters · doi:10.1103/physrevlett.124.020503 · Cited by 256
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
Vicentini, F., Hofmann, D., Szabó, A., Wu, D., Roth, C., Giuliani, C., Pescia, G., Nys, J., Vargas-Calderón, V., Astrakhantsev, N., et al. (2022) · SciPost Physics Codebases · doi:10.21468/scipostphyscodeb.7 · Cited by 135
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
Cheng, S., Chen, J., & Wang, L. (2018) · Entropy · doi:10.3390/e20080583 · Cited by 104
Metadata
- Authors
- Glasser, I., Pancotti, N., August, M., Rodriguez, I. D., & Cirac, J. I.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-01-11
- Volume
- 8
- Issue
- 1
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2762549329
- Cited by
- 214
- References
- 10
- FWCI
- 13.09
- Type
- journal article
- Language
- en
- Open access
- gold
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- Glasser, I., Pancotti, N., August, M., Rodriguez, I. D., & Cirac, J. I. (2018). Neural-Network Quantum States, String-Bond States, and Chiral Topological States. Physical Review X. https://doi.org/10.1103/physrevx.8.011006
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
