Deep Neural Network Detects Quantum Phase Transition

TL;DR

This paper demonstrates that a neural network can effectively detect quantum phase transitions in a one-dimensional Ising spin system by classifying spin configurations, providing a promising approach for analyzing quantum many-body systems.

Contribution

The study introduces a neural network-based method to identify quantum phase transitions from spin configurations, applicable to both simulated and experimental data.

Findings

Successfully classified the transverse field strength from spin configurations.

Accurately estimated the critical point of the model at $b3_c = J$.

Validated the method using quantum Monte Carlo simulation data.

Abstract

We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully classified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model $\Gamma_c =J$.