Quantum Convolutional Neural Networks are Effectively Classically Simulable

Understand

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML).

• In this work we tie their heuristic success to two facts.
• First, that when randomly initialized, they can only operate on the information encoded in low-bodyness measurements of their input states.
• And second, that they are commonly benchmarked on "locally-easy'' datasets whose states are precisely classifiable by the information encoded in these low-bodyness observables subspace.