QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
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2 Pith papers cite this work. Polarity classification is still indexing.
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PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.
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Quantum Convolutional Neural Networks are Effectively Classically Simulable
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.