A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.
Fast state tomography with optimal error bounds
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
A thresholding bandit algorithm on data from a single-parameter entanglement-witness family enables conclusive batch entanglement detection for two-qubit states in class F, with MAB-derived sample-complexity bounds.
citing papers explorer
-
Efficient Learning of Structured Quantum Circuits via Pauli Dimensionality and Sparsity
A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.
-
Batch Entanglement Detection in Parameterized Qubit States using Classical Bandit Algorithms
A thresholding bandit algorithm on data from a single-parameter entanglement-witness family enables conclusive batch entanglement detection for two-qubit states in class F, with MAB-derived sample-complexity bounds.