QFTLM computes thermal expectation values on quantum computers by merging quantum Krylov methods with efficient typical-state preparation for trace estimation.
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Survival probability maps for any nontrivial pair of unitaries cannot achieve point-wise complementary correlation over the full projective state space, imposing a unitary-geometric limit on anti-contrast.
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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Quantum Finite Temperature Lanczos Method
QFTLM computes thermal expectation values on quantum computers by merging quantum Krylov methods with efficient typical-state preparation for trace estimation.
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Fundamental limits to contrast reversal of survival probability correlations
Survival probability maps for any nontrivial pair of unitaries cannot achieve point-wise complementary correlation over the full projective state space, imposing a unitary-geometric limit on anti-contrast.
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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.