Deep Neural Network Detects Quantum Phase Transition

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 ${\Gamma }_c=J$.

4pages,3 figures

References in corpus (4)

• Machine learning phases of matter
• Detection of phase transition via convolutional neural network
• Locations of multicritical points for spin glasses on regular lattices
• Statistical-mechanical analysis of pre-training and fine tuning in deep learning

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