An autoregressive sampler draws Pauli strings sequentially from computable conditionals to enable linear-cost fidelity estimation for random matrix product states, with a grouped commuting extension to lower variance.
[CLSW26] Andrea Coladangelo, Jerry Li, Joseph Slote, and Ellen Wu
2 Pith papers cite this work. Polarity classification is still indexing.
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Algorithms achieve near-optimal quantum state certification with limited entanglement (t=d^2 copies), plus similar results for mixedness testing and purity estimation, supported by lower bounds.
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Verifying random matrix product states with autoregressive local measurements
An autoregressive sampler draws Pauli strings sequentially from computable conditionals to enable linear-cost fidelity estimation for random matrix product states, with a grouped commuting extension to lower variance.
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Optimal Quantum State Testing Even with Limited Entanglement
Algorithms achieve near-optimal quantum state certification with limited entanglement (t=d^2 copies), plus similar results for mixedness testing and purity estimation, supported by lower bounds.