Mixed-state escape bounds and proper agnostic learning of stabilizer states in odd-prime dimensions
Loading original first page…
Abstract
We prove mixed-input escape bounds for weighted Weyl measurements in every odd-prime local dimension. A purity-sensitive single-system inequality supplies the mixed-state estimate. Combined with stabilizer bootstrapping, coherent conditioning and bounded rejection sampling, it yields a proper additive agnostic learner with explicit resource bounds. For each fixed prime and a lower bound on stabilizer overlap, the resource dependence on inverse overlap is quasipolynomial.
Read the full paper in the original PDF.
Edition files
- main.pdf115.1 KiB · pdf
SHA-256
34e649f0533522a134290bc76c148ac29b237ccfdeccafa8fd6c74cafcf7b7a7 - main.tex29.0 KiB · source
SHA-256
e1d663515a0d79e0c05edab3e5eb16536ed4ce650b06158c951f5d58c7cf4699 - Odd-prime-stabilizer-learning-edition-1-privacy-corrected-20261019.zip137.3 KiB · package
SHA-256
a8312bfc15a9427d6744507dbb43fb43c7aa2e1b0f5c01764aeb57959d3b7772 - MANIFEST.sha256.json1.3 KiB · manifest
SHA-256
fc8858b745581731515f4037a2ac09e1c07e39659af083b2e904718f02742af5
Available editions
Research details
- Subjects
- Mathematics · Quantum Information Theory
- Keywords
- stabilizer states, agnostic learning, quantum learning, mixed states, odd-prime dimensions
- Manuscript date
- 2026-10-18
- Reader reuse
- No additional permission granted
Scope
Arbitrary mixed inputs in odd-prime dimension, in an exact Clifford and coherent-measurement model. The stated copy counts and resource bounds are sufficient. The qutrit case is included in this consolidated paper.
Reproduction
Corrected October 18, 2026 version. The complete package contains the manuscript, analytic proofs, verification programs, numerical diagnostics and checksum manifest. Reproduction instructions are in README.txt. Finite diagnostics supplement the analytic proofs.
Publication package corrected to contain publication materials only. The manuscript and edition are unchanged.