Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
arXiv:2110.05383 · doi:10.21468/SciPostPhys.12.3.107
Abstract
The detection of phase transitions in quantum many-body systems with lowest possible prior knowledge of their details is among the most rousing goals of the flourishing application of machine-learning techniques to physical questions. Here, we train a Generative Adversarial Network (GAN) with the Entanglement Spectrum of a system bipartition, as extracted by means of Matrix Product States ansätze. We are able to identify gapless-to-gapped phase transitions in different one-dimensional models by looking at the machine inability to reconstruct outsider data with respect to the training set. We foresee that GAN-based methods will become instrumental in anomaly detection schemes applied to the determination of phase-diagrams.
14 pages, 5 figures
References in corpus (13)
- Many-Body Physics with Ultracold Gases
- The density-matrix renormalization group in the age of matrix product states
- Ultracold atomic gases in optical lattices: mimicking condensed matter physics and beyond
- Entanglement Spectrum as a Generalization of Entanglement Entropy: Identification of Topological Order in Non-Abelian Fractional Quantum Hall Effect States
- Learning phase transitions by confusion
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Unsupervised machine learning of topological phase transitions from experimental data
- Machine-Learning Studies on Spin Models
- Density-Matrix Renormalization Group Study of Extended Kitaev-Heisenberg Model
- Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transitions in the q-state clock models
- Intrinsic dimension of path integrals: data mining quantum criticality and emergent simplicity
- Collisionless drag for a one-dimensional two-component Bose-Hubbard model
- Unsupervised mapping of phase diagrams of 2D systems from infinite projected entangled-pair states via deep anomaly detection
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