Random dimension reduction and learning symmetric properties of quantum states
Random dimension reduction is a method to simultaneously reduce many quantum states' dimensions while preserving properties invariant under tensor power actions of an isometry. This procedure simplifies learning symmetric properties, offering a black-box way to replace high dimensions with maximum sample complexity rank, improving bounds for estimating distances and fidelities between quantum states.