Neural networks identify quantum phases without post-selection

Researchers have devised a neural network capable of classifying quantum phases, specifically, distinguishing between ‘trivial,’ ‘long-range entangled,’ and ‘symmetry-protected topological’ states, directly from raw measurement outcomes. This new approach bypasses the need to reconstruct the complex quantum state itself, a computationally intensive process that previously limited the study of these transitions to small systems. The system’s success is signaled by a “sharp convergence in the classifier’s accuracy,” indicating the system has reached a steady-state phase without relying on the exponentially unfeasible process of Hui Yu and Jiangping Hu of the Chinese Academy of Sciences detail this robust, post-selection-free method for experimentally probing measurement-induced phases. This network classifies phases by directly analyzing raw measurement outcomes, sidestepping the need for multiple identical quantum trajectories traditionally required for observation of measurement-induced phase…