# Learning phase transitions by confusion

@article{Nieuwenburg2017LearningPT, title={Learning phase transitions by confusion}, author={Evert van Nieuwenburg and Ye-Hua Liu and Sebastian D. Huber}, journal={Nature Physics}, year={2017}, volume={13}, pages={435-439} }

A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list. Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever…

## 469 Citations

Machine learning phases of matter

- Computer Science
- 2016

It is shown that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians.

Quantum phase recognition via unsupervised machine learning

- Physics, Computer Science
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This work introduces a gener- alization of supervised machine learning approaches that allows to accurately map out phase diagrams of inter- acting many-body systems without any prior knowledge, e.g. of their general topology or the number of distinct phases.

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- Computer SciencePhysical Review B
- 2018

An analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer, and demonstrates how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks.

Detecting the Many-Body Localization Transition with Machine Learning Techniques

- Computer Science
- 2018

This thesis detects the phase transition with machine learning techniques applied to the entanglement spectra of eigenstates in disordered versions of the Heisenberg model and the transverse Ising model with neural networks to replicate the results of Refs.

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- PhysicsNature Physics
- 2019

This work employs an artificial neural network and deep-learning techniques to identify quantum phase transitions from single-shot experimental momentum-space density images of ultracold quantum gases and obtains results that were not feasible with conventional methods.

Machine Learning Out-of-Equilibrium Phases of Matter.

- PhysicsPhysical review letters
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This Letter shows that a single feed-forward neural network can decode the defining structures of two distinct MBL phases and a thermalizing phase, using entanglement spectra obtained from individual eigenstates and introduces a simplicial geometry-based method for extracting multipartite phase boundaries.

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- Computer ScienceArXiv
- 2019

A variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter and a majority vote quantum classifier built from a nearest-neighbor (checkerboard) quantum neural network are proposed.

Machine learning detection of Berezinskii-Kosterlitz-Thouless transitions in
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We demonstrate that a machine learning technique with a simple feedforward neural network can sensitively detect two successive phase transitions associated with the Berezinskii–Kosterlitz– Thouless…

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A general comprehensive machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology is developed.

Identifying topological order through unsupervised machine learning

- PhysicsNature Physics
- 2019

An unsupervised machine learning algorithm that identifies topological order is demonstrated and is shown to be capable of classifying samples of the two-dimensional XY model by winding number and capture the Berezinskii–Kosterlitz–Thouless transition.

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