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

Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang

TL;DR

The paper introduces stabilizer bootstrapping, a general framework for agnostic tomography that yields efficient protocols across several structured state classes. By iteratively collecting high-correlation stabilizers via Bell difference sampling and amplifying fidelity with low-correlation projections, the authors derive polynomial-to-quasi-polynomial time algorithms for agnostic tomography of stabilizer states, high-stabilizer-dimension states, discrete product states, and stabilizer-product states, with corresponding list-decoding guarantees. A key corollary is an efficient estimator for stabilizer fidelity (magic) in quasipolynomial time, enabling practical magic quantification in $n$-qubit systems. The paper also provides information-theoretic lower bounds and discusses limitations and open questions, including whether the 1/τ dependence can be tightened and how to extend the framework to broader state classes. Overall, stabilizer bootstrapping offers a unified, scalable approach to agnostic quantum state learning beyond realizable models, with impactful implications for quantum device characterization and quantum magic estimation.

Abstract

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 $\ge τ- ε$ 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 $\mathrm{poly}(n,1/ε)\cdot (1/τ)^{O(\log(1/τ))}$, answering an open question posed by Grewal, Iyer, Kretschmer, Liang [43] and Anshu and Arunachalam [6]. Previous protocols ran in time $\mathrm{exp}(Θ(n))$ or required $τ>\cos^2(π/8)$. States with stabilizer dimension $n - t$: We give a protocol that runs in time $n^3\cdot(2^t/τ)^{O(\log(1/ε))}$, 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 $C = K^{\otimes n}$ for some $μ$-separated discrete set $K$ of single-qubit states, we give a protocol that runs in time $(n/μ)^{O((1 + \log (1/τ))/μ)}/ε^2$. 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 $(n^2/ε^2)\cdot (1/τ)^{O(\log(1/τ))}$. As a corollary, we give the first protocol for estimating stabilizer fidelity, a standard measure of magic for quantum states, to error $ε$ in $n^3 \mathrm{quasipoly}(1/ε)$ time.

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

TL;DR

The paper introduces stabilizer bootstrapping, a general framework for agnostic tomography that yields efficient protocols across several structured state classes. By iteratively collecting high-correlation stabilizers via Bell difference sampling and amplifying fidelity with low-correlation projections, the authors derive polynomial-to-quasi-polynomial time algorithms for agnostic tomography of stabilizer states, high-stabilizer-dimension states, discrete product states, and stabilizer-product states, with corresponding list-decoding guarantees. A key corollary is an efficient estimator for stabilizer fidelity (magic) in quasipolynomial time, enabling practical magic quantification in -qubit systems. The paper also provides information-theoretic lower bounds and discusses limitations and open questions, including whether the 1/τ dependence can be tightened and how to extend the framework to broader state classes. Overall, stabilizer bootstrapping offers a unified, scalable approach to agnostic quantum state learning beyond realizable models, with impactful implications for quantum device characterization and quantum magic 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 quantum states prepared by circuits with few non-Clifford gates, which only applied in the realizable setting where [33, 40, 49, 66]. Discrete product states: If for some -separated discrete set of single-qubit states, we give a protocol that runs in time . 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 . As a corollary, we give the first protocol for estimating stabilizer fidelity, a standard measure of magic for quantum states, to error in time.
Paper Structure (61 sections, 67 theorems, 122 equations, 7 algorithms)

This paper contains 61 sections, 67 theorems, 122 equations, 7 algorithms.

Key Result

Theorem 1.2

Fix any $1 \geqslant \tau\geqslant\varepsilon\geqslant 0$. There is an algorithm that, given access to copies of a mixed state $\rho$ with $\max_{\ket{\phi'}\in \pazocal{C}}\bra{\phi'}\rho\ket{\phi'} \geqslant \tau$ for $\pazocal{C}$ the class of stabilizer states, outputs a stabilizer state $\ket{\

Theorems & Definitions (120)

  • Definition 1.1: Agnostic tomography
  • Theorem 1.2: Informal, see \ref{['thm:all_gamma_approximate_local_maximizer']} and \ref{['cor:agnostic_learning_stabilizer']}
  • Theorem 1.3: Informal, see Corollary \ref{['cor:fid_est']}
  • Remark 1.4: Implication for optimization landscape
  • Theorem 1.5: Informal, see \ref{['thm:agnostic_learning_high_stabilizer_dimension_states']}
  • Theorem 1.6: Informal, see \ref{['thm:product_base']} and \ref{['cor:product_base']}
  • Theorem 1.7: Informal, see \ref{['thm:stab_product_base']} and \ref{['cor:stab_product_base']}
  • Theorem 1.8: Informal, see \ref{['thm:info_lower']}
  • Remark 2.1
  • Definition 4.1: Fidelity of quantum states
  • ...and 110 more