Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks

@article{Patel2022UnsupervisedLO,
  title={Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks},
  author={Zakaria Patel and Ejaaz Merali and Sebastian Johann Wetzel},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.04051}
}
. We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful… 

References

SHOWING 1-6 OF 6 REFERENCES
In Advances in Neural Information Processing Systems
Bill Baird { Publications References 1] B. Baird. Bifurcation analysis of oscillating neural network model of pattern recognition in the rabbit olfactory bulb. In D. 3] B. Baird. Bifurcation analysis
Monte Carlo methods in statistical physics (Clarendon
  • 1999
Preprint http: //arxiv.org/abs/2002.02973v2) Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks30
  • 2020
Preprint http: //arxiv.org/abs/2002.02973v2) Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks28
  • 2020
Quantum 4 327 ISSN 2521-327X URL https://doi.org/10.22331/q-2020-09-21-327
  • 2020
Physical Review B Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese
  • Neural Networks29
  • 2020