Quantum Phase

Quantum phase research focuses on identifying and classifying different phases of matter in quantum systems, often using limited measurement data. Current efforts leverage machine learning techniques, including neural networks (e.g., convolutional and generative models), tensor networks, and kernel methods, to analyze experimental data from quantum simulators (e.g., Rydberg atom arrays, trapped ions) and predict quantum properties from short-range correlations. This interdisciplinary approach promises to accelerate the discovery of novel quantum phases and improve our understanding of complex many-body systems, with implications for materials science, quantum computing, and fundamental physics.