PhysicsarXiv

Ranked by formulaVerdict: Notable

Certifying quantum code and circuit distances at scale

Code and circuit distances characterize how strongly quantum error correction suppresses logical errors and guide code comparisons, schedule choices and resource estimates.

By Cheng, Guo, Pan

Score████░░░░░░4.3

Key numbers

  • 8×10⁵ fault mechanisms
  • 99.98% junction yield
  • 53% for perfect blocks alone

Caveats

  • Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Code and circuit distances characterize how strongly quantum error correction suppresses logical errors and guide code comparisons, schedule choices and resource estimates. Yet many published distances are only upper bounds from heuristic searches. Here, we express both distances as tropical partition functions and evaluate them with rank-based dynamic programming and weight-bounded search. Circuit reductions and relaxations shrink the problems, and proved lower bounds with explicit logical errors of matching weight certify exact values. On two benchmarks, our method certifies more instances than all other tested exact solvers combined. We certify distances for codes with up to 31,200 qubits, such as Google's modular hyperbolic codes, and for memory circuits with up to 8×10⁵ fault mechanisms, such as those of high-rate Cornucopia codes. Our audit of 26 recent papers identifies overestimated distances and non-tight upper bounds in 10 papers, with a maximum discrepancy of at least eight. Certified distances also guide circuit design and show which defective chips remain usable. In particular, we certify circuit distance 16 for IBM's two-gross memory circuits with the standard schedule, not the assumed 18, whereas redesigned schedules reach 18 at one noisy cycle, at the cost of one more timestep per cycle. At 99.98% junction yield, keeping gross-code memory blocks with dead checks that are certified at distance 10 or more raises the estimated usable share from 53% for perfect blocks alone to 94% with an improved schedule.

Bin Cheng, Naixu Guo, Feng Pan

The editor's rubric

Heuristic review

Leverage3 / 5
A method or resource many groups across the field will adopt within a year.
18% of this desk’s rubric
Magnitude3 / 5
Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
20% of this desk’s rubric
Evidence3 / 5
Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
22% of this desk’s rubric
Novelty2 / 5
A new combination of known ideas.
22% of this desk’s rubric
Trajectory3 / 5
A clear path to scale.
10% of this desk’s rubric
Stakes2 / 5
Benefits a professional community (practitioners, clinicians, engineers).
8% of this desk’s rubric

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (many tasks); gains (relative gain); verification (multiple benchmarks, code released); scale (scalable).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit
5.4 / 10
Weighted rubric, evidence-gated.
Adjusted merit
4.5 / 10
Shrunk toward the desk prior by editor confidence (38%).
Attention
0%
Citations, upvotes, points, mentions.
Freshness
94%
Half-life decay since publication.

No attention signals recorded yet.

The record

  • Reviewed by heuristic-v10 on Nov 6, 2026, 07:29 UTC. Paper type: empirical.
  • Categories: quant-ph
  • TOP, No.6 in the Physics edition of November 6, 2026.