Quantum neural networks to detect entanglement transitions in quantum many-body systems

Abstract

Abstract Quantum entanglement becomes increasingly complex to analyze in many-body systems due to exponential growth in complexity with system size. In this work, we explore the potential of quantum machine learning (QML) to circumvent this. Specifically, we train a parameterized quantum neural network (QNN) model to detect transitions in the entanglement properties of the ground state in a multi-spin Ising model. This approach enables the classification of different entanglement states and provides deeper insights into the behavior of entanglement under multi-spin interactions. Our results demonstrate that QML can effectively simplify the classification process and overcome the complexity challenges encountered by classical algorithms.