Polynomial Resource Classification of Quantum Circuit Familes via Classical Shadows
Andrew Maciejunes, Ross Gore, Sachin Shetty, Barry Ezell

TL;DR
This study compares measurement strategies for classifying quantum circuit families, revealing that local Z-basis correlations dominate discriminative power, with classical shadows being less effective at larger scales.
Contribution
It demonstrates that simple Z-basis measurements outperform more complex strategies, and provides a theoretical framework explaining the concentration of correlations in certain bases.
Findings
Z-only measurements outperform multi-basis and classical shadows across all qubits.
Nearest-neighbor Z strategy matches Z-only performance within 0.02 accuracy.
Classification accuracy collapses to near chance above 12 qubits with quadratic shot budget.
Abstract
We compare four polynomial-resource measurement strategies, (I) -basis-only, (II) nearest-neighbor (NN), (III) multi-basis (, , ), and (IV) classical shadows, for classifying three quantum circuit families: IQP, Clifford, and Clifford. We find -only measurements outperform multi-basis and classical shadows across all qubit counts and all four classifiers evaluated, and the -feature NN strategy matches -only to within in Random Forest accuracy. The best result is a Random Forest accuracy of at 4--5 qubits under -only ( for NN, for multi-basis, for shadows). All four strategies collapse to near-chance accuracy () above approximately 12 qubits under the quadratic shot budget . These findings indicate that the discriminative signal between these circuit families is concentrated…
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