Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
arXiv:2205.04051 · doi:10.1088/1367-2630/ac9c7a
Abstract
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 method to explore new and unknown phases of matter.
References in corpus (12)
- Many-Body Physics with Individually-Controlled Rydberg Atoms
- Learning phase transitions by confusion
- Quantum computing with neutral atoms
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Stochastic series expansion method for quantum Ising models with arbitrary interactions
- Machine learning vortices at the Kosterlitz-Thouless transition
- Entanglement by Path Identity
- Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
- Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
- Machine Learning Topological Phases with a Solid-state Quantum Simulator
- Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions
- Few-shot machine learning in the three-dimensional Ising model
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- Learning phase transitions by siamese neural network
- Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders