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This paper resolves the exact critical crossover for identifying unknown pure stabilizer states, establishing a definitive threshold where optimal success probability converges to an explicit series. By proving that the pretty-good measurement is exactly optimal for finite systems, the work provides precise integer-scale laws governing sample complexity under collective measurements without side information. This advancement clarifies fundamental limits in quantum state tomography and error correction protocols involving stabilizer ensembles.
Exact Critical Curve for Uniform Stabilizer-State Identification
arXiv:2610.00266v1 Announce Type: new Abstract: The sample complexity of identifying an unknown pure stabilizer state has been a longstanding problem in quantum information. In this paper, we resolve the entire exact critical crossover for the uniform ensemble of pure $n$-qubit stabilizer states under arbitrary collective measurements and without side information. For $k=n+c$, with fixed integer $c$, the optimal success probability converges to an explicit positive series $P_{\infty}(c)$. In particular, $P_{\infty}(0)=0.1760837896\ldots$, while its two tails obey a quadratic-exponential law below threshold and $1-P_{\infty}(c)\sim2^{-c}$ above threshold. The pretty-good measurement is exactly optimal for every finite $n$ and $k$. The proof diagonalizes the stabilizer Gram operator through Clifford tensor-power duality and a defect filtration, including the exceptional binary sectors. The resulting integer-scale law quantifies the $n+O(1)$ copy threshold.
Abstract:The sample complexity of identifying an unknown pure stabilizer state has been a longstanding problem in quantum information. In this paper, we resolve the entire exact critical crossover for the uniform ensemble of pure $n$-qubit stabilizer states under arbitrary collective measurements and without side information. For $k=n+c$, with fixed integer $c$, the optimal success probability converges to an explicit positive series $P_{\infty}(c)$. In particular, $P_{\infty}(0)=0.1760837896\ldots$, while its two tails obey a quadratic-exponential law below threshold and $1-P_{\infty}(c)\sim2^{-c}$ above threshold. The pretty-good measurement is exactly optimal for every finite $n$ and $k$. The proof diagonalizes the stabilizer Gram operator through Clifford tensor-power duality and a defect filtration, including the exceptional binary sectors. The resulting integer-scale law quantifies the $n+O(1)$ copy threshold.
Related arXiv paper: arXiv:2609.13923,"Partial Stabilizer Learning under Fixed Commuting Constraints and the Absence of a Copy-Rate Discount,"establishes the first-order copy threshold for unrestricted stabilizer-state identification, but leaves the fixed-offset regime k=n+c untreated. This submission resolves that regime by determining the limiting optimal success probability
arXiv:2610.00266 [quant-ph]
(or arXiv:2610.00266v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.00266 arXiv-issued DOI via DataCite (pending registration)
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