For an explicit prefix/tree family of quantum states, adaptive local Pauli tomography achieves polynomial copy complexity while non-adaptive strategies require exponentially many copies.
When does adap- tivity help for quantum state learning?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MLE for 1D-local sparse Pauli-Lindblad channels reduces to an efficient Bayesian network computation, yielding improved tomography.
citing papers explorer
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An Exponential Advantage for Adaptive Tomography of Structured States under Pauli Basis Measurements
For an explicit prefix/tree family of quantum states, adaptive local Pauli tomography achieves polynomial copy complexity while non-adaptive strategies require exponentially many copies.
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Better Pauli Channel Learning with Maximum Likelihood Estimation
MLE for 1D-local sparse Pauli-Lindblad channels reduces to an efficient Bayesian network computation, yielding improved tomography.