Improved upper bounds on the stabilizer rank of magic states
Bibliographic record
Abstract
In this work we improve the runtime of recent classical algorithms for strong simulation of quantum circuits composed of Clifford and T gates. The improvement is obtained by establishing a new upper bound on the stabilizer rank of m copies of the magic state | T ⟩ = 2 − 1 ( | 0 ⟩ + e i π / 4 | 1 ⟩ ) in the limit of large m . In particular, we show that | T ⟩ ⊗ m can be exactly expressed as a superposition of at most O ( 2 α m ) stabilizer states, where α ≤ 0.3963 , improving on the best previously known bound α ≤ 0.463 . This furnishes, via known techniques, a classical algorithm which approximates output probabilities of an n -qubit Clifford + T circuit U with m uses of the T gate to within a given inverse polynomial relative error using a runtime p o l y ( n , m ) 2 α m . We also provide improved upper bounds on the stabilizer rank of symmetric product states | ψ ⟩ ⊗ m more generally; as a consequence we obtain a strong simulation algorithm for circuits consisting of Clifford gates and m instances of any (fixed) single-qubit Z -rotation gate with runtime poly ( n , m ) 2 m / 2 . We suggest a method to further improve the upper bounds by constructing linear codes with certain properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".