Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
Not on the map. Why?
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.
Builds on
11 of 66 references.
References · 11
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
- 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
- 2016Efficient quantum tomography IIcited by 54
- 2021Exponential separations between learning with and without quantum memorycited by 50
- 2020Sample-efficient learning of quantum many-body systemscited by 36
- 2023A survey on the complexity of learning quantum statescited by 31
- 2023Learning quantum Hamiltonians at any temperature in polynomial timecited by 20
- 2024Learning shallow quantum circuitscited by 20
- 2017Quantum state certificationcited by 13
- 2023Improved Stabilizer Estimation via Bell Difference Samplingcited by 11
- 2021Optimal learning of quantum Hamiltonians from high-temperature Gibbs statescited by 8
Built on by
Citing papers per year: 2024: 2; 2025: 3.
5 papers.
- 2024Testing classical properties from quantum datacited by 1
- 2025Mildly-Interacting Fermionic Unitaries are Efficiently Learnablecited by 1
- 2024The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubitscited by 0
- 2025Efficient learning of bosonic Gaussian unitariescited by 0
- 2025Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and morecited by 0
- 2025Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more8 Oct 2025
- 2025Efficient learning of bosonic Gaussian unitaries7 Oct 2025
- 2025Mildly-Interacting Fermionic Unitaries are Efficiently Learnable15 Apr 2025
- 2024The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits16 Dec 2024
- 2024Testing classical properties from quantum data19 Nov 2024