Fragmentation is Efficiently Learnable by Quantum Neural Networks
This work explores classifying quantum states into fragmented subspaces using a Quantum Neural Network (QNN). The QNN learns to identify which low-dimensional subspace a given state belongs to by minimizing a specific loss function. It proves that this learning is efficient on quantum computers if the fragmentation phenomenon meets certain conditions, overcoming classical simulation barriers.