Formal Problem Definition

Inputs

For each experiment point:

  • code family/configuration (distance, rounds, supports),
  • noise parameters (p, sigma, or hybrid parameters),
  • decoder choice,
  • RNG seed and trial count,
  • request/response datasets in NDJSON form (for replay workflows).

State Variables

  • physical error vectors (e_X, e_Z),
  • syndromes (s_X, s_Z),
  • decoder corrections (c_X, c_Z),
  • residual logical predicates.

Output Variables

  • per-run decoder metrics (flip count, warning rates, event rates),
  • logical error estimates,
  • confidence intervals,
  • threshold/scaling summaries,
  • reproducible tables/figures/manifests.

Primary Objective

Estimate logical failure behavior under controlled noise and decoding choices, then compare:

  1. across decoders,
  2. across software stacks (PennyLane/Qiskit/Cirq vs LiDMaS+ reference),
  3. across code families (surface, gkp),
  4. across execution surfaces (CLI vs App).

Constraint Set

  • deterministic seeds for comparable reruns,
  • stable dataset schema for replay compatibility,
  • per-step artifact outputs required for auditability,
  • explicit failure/warning accounting rather than silent omission.