Does Data Complexity Predict Quantum Advantage? A framework, a pre-specified test, and an attribution of the measured edge
The paper proposes a framework to quantify data complexity, which determines if quantum machine learning offers an advantage over classical methods. It defines complexity using both classical and quantum measures like entanglement and topological invariants. This framework suggests that quantum advantage depends on matching model expressibility to the dataset's structural richness, not just qubit count.