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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The learning to synthesize method produces high-resolution, artifact-free phase reconstructions resilient to low photon flux by separately learning low and high frequency bands and then synthesizing them.
citing papers explorer
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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.
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Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
The learning to synthesize method produces high-resolution, artifact-free phase reconstructions resilient to low photon flux by separately learning low and high frequency bands and then synthesizing them.