Published In
Quantum
Document Type
Pre-Print
Publication Date
3-13-2026
Subjects
Agnostic Tomography -- Quantum Learning
Abstract
We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state ρ and a class of quantum states C, the goal is to output a succinct description of a state that approximates ρ at least as well as any state in C (up to some small error ε). This task generalizes ordinary quantum tomography of states in C and is more challenging because the learning algorithm must be robust to perturbations of ρ. We give an efficient agnostic tomography algorithm for the class C of n-qubit stabilizer product states. Assuming ρ has fidelity at least τ with a stabilizer product state, the algorithm runs in time nO(log(2/τ))/ε2, which is poly(n/ε) for any constant τ .
Rights
Copyright (c) 2026 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.22331/q-2026-03-13-2027
Persistent Identifier
https://archives.pdx.edu/ds/psu/44555
Citation Details
Published as: Grewal, S., Iyer, V., Kretschmer, W., & Liang, D. (2026). Agnostic tomography of stabilizer product states. Quantum, 10, 2027.
Description
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as: (2026). Agnostic tomography of stabilizer product states. Quantum, 10, 2027.