Exact results show U(1) symmetry substantially suppresses non-stabilizerness in random states, with different leading scaling from entanglement near zero charge density.
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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.
Quantum speedup stems from matching problem structure to interference patterns under constraints of measurement contexts, thermodynamic irreversibility, and contextuality, rather than from parallel computation.
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Non-stabilizerness and U(1) symmetry in chaotic many-body quantum systems
Exact results show U(1) symmetry substantially suppresses non-stabilizerness in random states, with different leading scaling from entanglement near zero charge density.
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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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The Physical and Contextual Limits of Quantum Speedup
Quantum speedup stems from matching problem structure to interference patterns under constraints of measurement contexts, thermodynamic irreversibility, and contextuality, rather than from parallel computation.