# Machine learning of quantum phase transitions

@article{Dong2019MachineLO, title={Machine learning of quantum phase transitions}, author={Xiao-yu Dong and F. Pollmann and Xue-Feng Zhang}, journal={Physical Review B}, year={2019} }

Machine learning algorithms provide a new perspective on the study of physical phenomena. In this paper, we explore the nature of quantum phase transitions using multi-color convolutional neural-network (CNN) in combination with quantum Monte Carlo simulations. We propose a method that compresses $d+1$ dimensional space-time configurations to a manageable size and then use them as the input for a CNN. We test our approach on two models and show that both continuous and discontinuous quantum…

## 27 Citations

Revealing quantum chaos with machine learning

- Computer Science, PhysicsArXiv
- 2019

It is shown that machine-learning techniques allow us to pin down the transition from integrability to many-body quantum chaos in Heisenberg XXZ spin chains and the existence of universal W shapes that characterize the transition is confirmed.

Classifying snapshots of the doped Hubbard model with machine learning

- Physics, Computer ScienceNature Physics
- 2019

This work compares the data from an experimental realization of the two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped quantum spin liquid state of resonating valence bond type, and the geometric string theory, describing a state with hidden spin order.

Neural networks in quantum many-body physics: a hands-on tutorial

- Physics
- 2021

Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks…

Model-Independent Quantum Phases Classifier

- Physics
- 2021

Machine learning has revolutionized many fields of science and technology. Through the k-Nearest Neighbors algorithm, we develop a model-independent classifier, where the algorithm can classify…

Unveiling phase transitions with machine learning

- Physics, MathematicsPhysical Review B
- 2019

The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities…

Learning spin liquids on a honeycomb lattice with artificial neural networks

- MedicineScientific reports
- 2021

Investigation of the ability of the machine learning method based on the restricted Boltzmann machine in capturing physical quantities including the ground-state energy, spin-structure factor, magnetization, quantum coherence, and multipartite entanglement in the two-dimensional ferromagnetic spin liquids on a honeycomb lattice finds that it can encode the many-body wavefunction quite well.

Probing transport in quantum many-fermion simulations via quantum loop topography

- PhysicsPhysical Review B
- 2019

Quantum many-fermion systems give rise to diverse states of matter that often reveal themselves in distinctive transport properties. While some of these states can be captured by microscopic models…

Robust identification of topological phase transition by self-supervised machine learning approach

- Physics
- 2021

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local…

Kernel methods in Quantum Machine Learning

- Computer ScienceQuantum Mach. Intell.
- 2019

The latest developments regarding the usage of quantum computing for a particular class of machine learning algorithms known as kernel methods known as Kernel methods are reviewed.

A universal neural network for learning phases and criticalities.

- Computer Science, Physics
- 2021

It is favorably probable that much simpler but yet elegant machine learning techniques can be constructed for fields of many-body systems other than the critical phenomena.

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