A dual-valued phase shifter in linear optics creates variational cost landscapes with fewer local minima and outperforms prior linear-optical variational algorithms by mitigating barren plateaus.
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2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Mitigating the barren plateau problem in linear optics
A dual-valued phase shifter in linear optics creates variational cost landscapes with fewer local minima and outperforms prior linear-optical variational algorithms by mitigating barren plateaus.
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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.