Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang

TL;DR
This paper introduces stabilizer bootstrapping, a new efficient framework for agnostic quantum state tomography across various classes, improving runtime and capabilities for states like stabilizer states, low stabilizer dimension states, and product states.
Contribution
The paper presents stabilizer bootstrapping, enabling computationally efficient agnostic tomography protocols for multiple quantum state classes, answering open questions and extending prior work.
Findings
New polynomial-time protocols for stabilizer states tomography.
Extended tomography methods for states with limited stabilizer dimension.
First efficient protocol for stabilizer fidelity estimation.
Abstract
We study the task of agnostic tomography: given copies of an unknown -qubit state which has fidelity with some state in a given class , find a state which has fidelity with . We give a new framework, stabilizer bootstrapping, for designing computationally efficient protocols for this task, and use this to get new agnostic tomography protocols for the following classes: Stabilizer states: We give a protocol that runs in time , answering an open question posed by Grewal, Iyer, Kretschmer, Liang [43] and Anshu and Arunachalam [6]. Previous protocols ran in time or required . States with stabilizer dimension : We give a protocol that runs in time , extending recent work on learning…
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Taxonomy
TopicsReservoir Engineering and Simulation Methods
MethodsSparse Evolutionary Training
