Topological Persistence Machine of Phase Transitions
- URL: http://arxiv.org/abs/2004.03169v3
- Date: Tue, 30 Mar 2021 07:41:12 GMT
- Title: Topological Persistence Machine of Phase Transitions
- Authors: Quoc Hoan Tran, Mark Chen, and Yoshihiko Hasegawa
- Abstract summary: Topological data analysis is an emerging framework for characterizing the shape of data.
We propose a general framework, termed "topological persistence machine," to construct the shape of data from correlations in states.
We demonstrate the efficacy of the approach in detecting the Berezinskii--Kosterlitz--Thouless phase transition in the classical XY model.
- Score: 7.553620028719304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The study of phase transitions using data-driven approaches is challenging,
especially when little prior knowledge of the system is available. Topological
data analysis is an emerging framework for characterizing the shape of data and
has recently achieved success in detecting structural transitions in material
science, such as the glass--liquid transition. However, data obtained from
physical states may not have explicit shapes as structural materials. We thus
propose a general framework, termed "topological persistence machine," to
construct the shape of data from correlations in states, so that we can
subsequently decipher phase transitions via qualitative changes in the shape.
Our framework enables an effective and unified approach in phase transition
analysis. We demonstrate the efficacy of the approach in detecting the
Berezinskii--Kosterlitz--Thouless phase transition in the classical XY model
and quantum phase transitions in the transverse Ising and Bose--Hubbard models.
Interestingly, while these phase transitions have proven to be notoriously
difficult to analyze using traditional methods, they can be characterized
through our framework without requiring prior knowledge of the phases. Our
approach is thus expected to be widely applicable and will provide practical
insights for exploring the phases of experimental physical systems.
Related papers
- Cascade of phase transitions in the training of Energy-based models [9.945465034701288]
We investigate the feature encoding process in a prototypical energy-based generative model, the Bernoulli-Bernoulli RBM.
Our study tracks the evolution of the model's weight matrix through its singular value decomposition.
We validate our theoretical results by training the Bernoulli-Bernoulli RBM on real data sets.
arXiv Detail & Related papers (2024-05-23T15:25:56Z) - Machine learning phase transitions: Connections to the Fisher
information [0.0]
We show that machine-learning indicators of phase transitions approximate the square root of the system's (quantum) Fisher information from below.
We numerically demonstrate the quality of these bounds for phase transitions in classical and quantum systems.
arXiv Detail & Related papers (2023-11-17T18:59:35Z) - Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
We propose to model latent structures with a learned dynamic potential landscape.
Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations.
Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
arXiv Detail & Related papers (2023-04-25T15:53:45Z) - Out-of-Time-Order Correlation as a Witness for Topological Phase
Transitions [10.799933392186222]
We propose a physical witness for dynamically detecting topological phase transitions (TPTs) via an experimentally observable out-of-time-order correlation (OTOC)
The distinguishable OTOC dynamics appears in the topological trivial and non-trivial phases due to the topological locality.
Our work fundamentally brings the OTOC in the realm of TPTs, and offers the prospect of exploring new topological physics with quantum correlations.
arXiv Detail & Related papers (2023-02-02T07:57:22Z) - Topological transitions of the generalized Pancharatnam-Berry phase [55.41644538483948]
We show that geometric phases can be induced by a sequence of generalized measurements implemented on a single qubit.
We demonstrate and study this transition experimentally employing an optical platform.
Our protocol can be interpreted in terms of environment-induced geometric phases.
arXiv Detail & Related papers (2022-11-15T21:31:29Z) - Probing the topological Anderson transition with quantum walks [48.7576911714538]
We consider one-dimensional quantum walks in optical linear networks with synthetically introduced disorder and tunable system parameters.
The option to directly monitor the walker's probability distribution makes this optical platform ideally suited for the experimental observation of the unique signatures of the one-dimensional topological Anderson transition.
arXiv Detail & Related papers (2021-02-01T21:19:15Z) - Unsupervised machine learning of topological phase transitions from
experimental data [52.77024349608834]
We apply unsupervised machine learning techniques to experimental data from ultracold atoms.
We obtain the topological phase diagram of the Haldane model in a completely unbiased fashion.
Our work provides a benchmark for unsupervised detection of new exotic phases in complex many-body systems.
arXiv Detail & Related papers (2021-01-14T16:38:21Z) - Universality of entanglement transitions from stroboscopic to continuous
measurements [68.8204255655161]
We show that the entanglement transition at finite coupling persists if the continuously measured system is randomly nonintegrable.
This provides a bridge between a wide range of experimental settings and the wealth of knowledge accumulated for the latter systems.
arXiv Detail & Related papers (2020-05-04T21:45:59Z) - Topological Phase Transitions Induced by Varying Topology and Boundaries
in the Toric Code [0.0]
We study the sensitivity of such phases of matter to the underlying topology.
We claim that these phase transitions are accompanied by broken symmetries in the excitation space.
We show that the phase transition between such steady states is effectively captured by the expectation value of the open-loop operator.
arXiv Detail & Related papers (2020-04-07T18:00:06Z) - Unsupervised machine learning of quantum phase transitions using
diffusion maps [77.34726150561087]
We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning complex phase transitions unsupervised.
This method works for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators.
arXiv Detail & Related papers (2020-03-16T18:40:13Z) - Topological quantum phase transitions retrieved through unsupervised
machine learning [2.778293655629716]
We show that the unsupervised manifold learning can successfully retrieve topological quantum phase transitions in momentum and real space.
We demonstrate this method on the prototypical Su-Schefferri-Heeger (SSH) model, the Qi-Wu-Zhang (QWZ) model, and the quenched SSH model in momentum space.
arXiv Detail & Related papers (2020-02-06T17:11:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.