mlsanityData to 7 Oct 2026

Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation

arXiv 2408.06967 ↗First posted 13 Aug 2024quant-ph cs.CC cs.DScited by 5

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Abstract (arXiv)

We study the task of agnostic tomography: given copies of an unknown n-qubit state ρ which has fidelity τ with some state in a given class C, find a state which has fidelity ≥ τ - ε with ρ. 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 poly(n,1/ε)· (1/τ)O(log(1/τ)), answering an open question posed by Grewal, Iyer, Kretschmer, Liang [43] and Anshu and Arunachalam [6]. Previous protocols ran in time exp(Theta(n)) or required τ>cos2(π/8). States with stabilizer dimension n - t: We give a protocol that runs in time n3·(2t/τ)O(log(1/ε)), extending recent work on learning quantum states prepared by circuits with few non-Clifford gates, which only applied in the realizable setting where τ = 1 [33, 40, 49, 66]. Discrete product states: If C = Kotimes n for some μ-separated discrete set K of single-qubit states, we give a protocol that runs in time (n/μ)O((1 + log (1/τ))/μ)/ε2. This strictly generalizes a prior guarantee which applied to stabilizer product states [42]. For stabilizer product states, we give a further improved protocol that runs in time (n2/ε2)· (1/τ)O(log(1/τ)). As a corollary, we give the first protocol for estimating stabilizer fidelity, a standard measure of magic for quantum states, to error ε in n3 quasipoly(1/ε) time.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 66 references that are in the map. First posted on arXiv 13 Aug 2024.

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11 of 66 references.

References · 11

  1. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  2. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  3. 2016Efficient quantum tomography IIcited by 54
  4. 2021Exponential separations between learning with and without quantum memorycited by 50
  5. 2020Sample-efficient learning of quantum many-body systemscited by 36
  6. 2023A survey on the complexity of learning quantum statescited by 31
  7. 2023Learning quantum Hamiltonians at any temperature in polynomial timecited by 20
  8. 2024Learning shallow quantum circuitscited by 20
  9. 2017Quantum state certificationcited by 13
  10. 2023Improved Stabilizer Estimation via Bell Difference Samplingcited by 11
  11. 2021Optimal learning of quantum Hamiltonians from high-temperature Gibbs statescited by 8

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Citing papers per year: 2024: 2; 2025: 3.

5 papers.