Deep Neural Network Detects Quantum Phase Transition
This work addresses the challenge of identifying phase transitions in quantum systems, which is incremental as it applies an existing neural network method to a new domain with specific data.
The authors tackled the problem of detecting quantum phase transitions by mapping spin configurations from a quantum many-body system onto a neural network, achieving successful classification of transverse field strength and consistent estimation of the critical point at Γ_c = J.
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 $Γ_c =J$.