The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Learning thermodynamics with Boltzmann machines
Open accessAbstract
A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we develop a Boltzmann machine that is capable of modeling thermodynamic observables for physical systems in thermal equilibrium. Through unsupervised learning, we train the Boltzmann machine on data sets constructed with spin configurations importance sampled from the partition function of an Ising Hamiltonian at different temperatures using Monte Carlo (MC) methods. The trained Boltzmann machine is then used to generate spin states, for which we compare thermodynamic observables to those computed by direct MC sampling. We demonstrate that the Boltzmann machine can faithfully reproduce the observables of the physical system. Further, we observe that the number of neurons required to obtain accurate results increases as the system is brought close to criticality.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879
Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B., & Melko, R. (2018) · Physical Review X · doi:10.1103/physrevx.8.021050 · Cited by 520
Self-citation
Cited by
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
Neural-network quantum state tomography
Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R., & Carleo, G. (2018) · Nature Physics · doi:10.1038/s41567-018-0048-5 · Cited by 843
Self-citationA Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
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
Quantum Entanglement in Neural Network States
Deng, D.-L., Li, X., & Das Sarma, S. (2017) · Physical Review X · doi:10.1103/physrevx.7.021021 · Cited by 403
Metadata
- Authors
- Torlai, G., & Melko, R. G.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2016-10-17
- Volume
- 94
- Issue
- 16
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2418689459
- Cited by
- 252
- References
- 5
- FWCI
- 19.90
- Type
- journal article
- Language
- en
- Open access
- green
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