Pre-registration, deposited before any outcome file of the dataset was analysed. It fixes the method and decision rules for an independent replication, with open-source Stim and PyMatching, of the surface-code scaling result in Google Quantum AI's dataset 10.5281/zenodo.6804040 (arXiv:2207.06431): R1 reproducing the provided PyMatching predictions; R2 whether distance-5 logical error per round is below the distance-3 average (Λ > 1) with data-fitted error models; R3 whether the abstract's per-cycle error rates are reproduced from the provided predictions of each decoder. Results will be published whichever way they fall.
Pre-registration, deposited before any outcome file of the dataset was analysed. It fixes the method and decision rules for an independent replication, with open-source Stim and PyMatching, of the surface-code scaling result in Google Quantum AI's dataset 10.5281/zenodo.6804040 (arXiv:2207.06431): R1 reproducing the provided PyMatching predictions; R2 whether distance-5 logical error per round is below the distance-3 average (Λ > 1) with data-fitted error models; R3 whether the abstract's per-cycle error rates are reproduced from the provided predictions of each decoder. Results will be published whichever way they fall.
Version 2: pre-registration addendum for Phase D Outcome of the registered gate (G0) The G0 gate failed for every dataset, window family and null. No twin built from independent error mechanisms with circuit-local supports reproduces the bulk of the window-count distributions in the public data from Google Willow, Google Sycamore or Rigetti. For larger codes the twin cannot even be simulated. As the protocol requires, hypotheses H1–H3 are therefore not tested. This version adds pre-registration addendum 1. It fixes the analyses run instead, before either touches the test halves: • Model-free certificate (T3), on the test halves. It covers every registered configuration that has a circuit (the Google archives and Rigetti D6e). Windows are whole-patch windows of 1 and of 5 rounds, separated by one unused round, and the disjointness of their fault-location sets is checked from each circuit. Each stratum (archive, code family, code distance, window family) gets a premise curve ε_lo(k) on the grid k = c·j, j = 1–16, with Bonferroni correction at family-wise α = 0.05.• Threshold values for the certificate. ε* = 7.4 × 10⁻⁴ (Fowler, PRL 109, 180502, 2012) is the primary value; 2.73 × 10⁻⁵ (Aliferis, Gottesman and Preskill, QIC 6, 97, 2006) is a sensitivity check. Certificates against ε* are computed for surface-code strata only. Repetition-code, colour-code and stability strata get premise curves only.• Bursts. The primary analysis uses all windows; a secondary one excludes the windows flagged by the frozen burst classifier. B1/B2 event rates are reported descriptively.• Cat-qubit case study (exploratory). The model-based premise meter is applied to the AWS cat-qubit (D4) distance-3 configurations whose twin was simulable in G0. It reports R(k) intervals and k_cert, with the H1 reading (consistent if R_hi ≤ 3 for all k ≤ k_cert). It is labelled exploratory because D4's own G0 verdict is a fail. Status of the data when version 2 was created G0, which uses only the bulk bins, has run on the test halves. Its stored histograms include tail bins, but no tail statistic has been computed or examined. Both new analyses were dry-run on fit halves as pipeline checks. One choice was made after that dry run, and it is recorded in the addendum: ε* certificates were limited to surface-code strata, and strata were split by code family. The dry run had shown repetition-code and stability strata crossing ε* at k = c, i.e. at their base error rate, where Fowler's surface-code theorem does not apply. The model-based certification horizon is not computed, because it needs a twin tail. Files added in version 2 • phaseD_addendum.json: the machine-readable addendum. SHA-256: a458bcb40aa474d94a882a563a38a1a152d7bfb4404df9299f99fb020ab78834• ADDENDUM.md: the readable version of the addendum.• qpremise_code_phaseD_ade59b0.zip: the code that will run. The addendum's code_sha256 (794122ae1557c009664127e1c2e6b109781d5a71efcc566bc637b991c0403ab6) can be recomputed from it as the SHA-256 over the files matching qpremise/**/*.py, phaseD/scripts/*.py and phaseC/scripts/run_g0.py, taken in path order: each file's relative path, then its bytes with LF line endings.• SHA256SUMS_addendum.txt: checksums of the three files above. The version 1 files are unchanged. The original registration text follows. ──────────────────────────── Version 1 registration (2026-10-01) What this registers The analysis protocol of the premise meter. It tests whether public quantum-error-correction syndrome data agree with an independent-noise "twin" fitted to the same data. It also measures how far each dataset can certify the local-stochastic premise of the threshold theorem. Every analysis choice is fixed in frozen_protocol.json:• configuration eligibility, and the pre-registered subset in g0_selection.json;• the interleaved fit/test split;• the window families;• twin construction: candidate mechanisms from each circuit, an exact parity-moment fit, and a signed quasi-probability simulation with local weights;• the G0 gate decision rule;• the burst classifiers B1 and B2 with their validation targets;• the excess-tail-ratio interval and the certification-horizon conditions;• the model-free certificate's window rule. qpremise_code_at_freeze_79dbf14.zip holds the analysis code at the freeze. The protocol's code_sha256 can be recomputed from it: the SHA-256 over every qpremise/**/*.py file in path order, each file's relative path followed by its bytes with LF line endings. Hypotheses analysed after this registration (on test halves only) H1. After removing windows flagged by the frozen burst classifier, the excess tail ratio R(k) = P_obs(K ≥ k) / P_twin(K ≥ k) has an upper 95% confidence bound ≤ 3 for all k ≤ k_cert. Failure: a lower bound > 3 that replicates on the held-out half. H2. Including burst windows, R(k) > 10 for k ≥ k_b in at least one dataset, and a burst-mixture model fitted to the flagged windows predicts the published repetition-code error floor within a factor of 10. H3. The empirical horizon (power ≥ 0.8 at α = 0.05 against a factor-5 excess) agrees with the exact binomial computation within ±1. At least one dataset certifies k ≥ 10 in whole-patch windows. These are evaluated only on (dataset, window family) pairs that pass the G0 gate under both the stationary (N1) and drift-aware (N5) twins. The model-free certificate (T3) does not depend on the twin. Its threshold ε* is fixed before any certificate is computed. Timing and what had been seen Frozen 2026-10-01 17:31 IST (git commit 79dbf14). Before the freeze, only fit halves had been analysed. The G0 gate (bulk of the window-count distributions only; decision rule frozen) began on the test halves minutes after the freeze. When this record was created, no tail statistic (R(k), H1–H3, burst rates, certificates) had been computed from the test halves, and their tail bins had not been examined. The planned cloud-hardware contrast experiment (H4, H5) is not part of this registration and will be registered separately before any hardware run. Full details are in PREREGISTRATION.md. Protocol SHA-256: 5f7458cfdbe0e203fd9e2bf5d579703509c8374bae53096c37f8f1530540707a
QECTOR Decoder Benchmark Datasets & Empirical Reports This repository hosts the evolving collection of reproducible benchmark datasets, JSON measurement artifacts, and empirical performance reports for the QECTOR Decoder v3 platform (Rust/Python quantum error-correction decoder). Each versioned deposit contains environment-stamped, content-hashed JSON artifacts together with the corresponding publication-grade report. All numerical results are fully regenerable from the published wheels and the scripts listed in the reproducibility sections. Scope across versions Code-capacity and circuit-level (Stim rotated_memory_z) evaluations Throughput (shots/s, measure-s/shot), logical error rates with 95 % Wilson intervals, syndrome faithfulness (GF(2) exact check), and CUDA-vs-CPU bit identity Decoder families: FastUnionFind, UnionFind, Blossom (exact MWPM), SparseBlossom, Batch/CPUBatch, BPOSD, HybridCascade, AmbiguityCluster, LookupTable, and CUDABatch (unweighted + DEM-weighted) Hardware provenance, licence-tier disclosure (GPU gated behind Enterprise), and SHA-256 inventories of every backing artifact Version history (selected) v0.6.6 (July 2026) – Workbench Benchmark Master Report: 1 858 decode-timing measurements from 105 runs, six code topologies (Heavy Hex, Repetition, Ring, Rotated Surface, Toric, Unrotated Surface), fixed p = 0.05, distances d = 3–19. Focus on raw latency, harmonic-mean throughput and peak memory. v0.6.8 - Empirical edge-hardware benchmarks. v0.7.0 (August 2026) – Full Empirical Decoder Benchmark Report on the published wheel: nine CPU families + CUDA batch path, circuit-level LER parity with PyMatching 2.4.0, exact syndrome faithfulness, DEM-weighted GPU scaling restoration, and complete artifact inventory (Appendix A). All figures and tables in the accompanying reports are regenerated from the JSON files listed with their SHA-256 hashes; no pre-release numbers are carried forward. Related resources PyPI: qector-decoder-v3 Repository: https://github.com/GuillaumeLessard/qector-decoder Website: https://www.qector.store
FINDING: Quantum error correction (QEC) milestone — Google AI demonstrates error suppression below threshold, enabling scalable logical qubits. | MATH: Key threshold constant: error rate per operation p<pth≈0.57% (surface code). Logical error rate scales as pL∝(p/pth)(d+1)/2 for code distance d. | CONNECTION: Surface code is a topological lattice (square grid) with Z2 symmetry — related to toric code, Kitaev's anyon model. Lattice structure: 4-fold rotational symmetry, no direct golden ratio. | DEPTH: 8 — foundational for fault-tolerant quantum computing, but no new geometric constant revealed. FINDING: Deltaflow — a full-stack QEC system achieving >10^4 reliable quantum operations (QuOps). | MATH: Logical fidelity F=1−ϵL, with ϵL≈c⋅(p/pth)d/2. Target: 106 QuOps for useful computation. | CONNECTION: Error suppression follows power law in code distance — Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
FINDING: Quantum error correction (QEC) milestone — Google AI demonstrates scaling of logical qubit fidelity below physical qubit error threshold, enabling fault-tolerant quantum computation. | MATH: Key threshold: physical error rate p<pth≈0.57% for surface code; logical error rate pL∝(p/pth)(d+1)/2 where d is code distance; stabilizer formalism uses Pauli group Pn and syndrome extraction via parity checks. | CONNECTION: Surface code is a topological QEC code based on a 2D square lattice (crystallographic symmetry p4m); code distance d corresponds to lattice size; logical operators are non-contractible loops on torus (homology). No direct golden ratio or base-60 link. | DEPTH: 8 — QEC is essential for scalable quantum computing; surface code threshold is a precise mathematical condition; topological nature ties to lattice geometry and homology, but no new fundamental constants or ratios emerge. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
This record bundles two engagement artifacts derived from the v1.4 register of the Trapped-Ion Ultrametric Testbed (10.5281/zenodo.22025544): (1) industry-brief-2026-08-20.md — "Why Measure Error Correlations Before Choosing a Code": a one-page brief answering whether the ultrametric quantum error-correction path is worth pursuing given its published independent-error threshold disadvantage (~55x below surface codes). The brief leads with the honest disclosure, frames the single falsifiable bet (error structure at scale is not independent), and offers four theory-neutral instruments (an 8-week trapped-ion probe, a laptop benchmark, an energy-audit template, and a QEC-Darwinism constraint checker). (2) correlated-error-benchmark-spec-2026-08-20.md — a pre-registered measurement protocol with six observables, five metrics, and three binding decision rules (including KILL-ULTRAMETRIC, which is binding on the program itself). The protocol is theory-neutral: the correlation metrics improve any code choice, ultrametric or not. Both artifacts are published as open engagement documents; the register's decision rules and kill-conditions are pre-registered. No hardware-advantage claim is made in either document.
FINDING: Quantum error correction (QEC) milestone — Google AI demonstrates error suppression below surface code threshold, enabling logical qubit operation with reduced physical error rates. | MATH: Surface code distance d yields logical error rate PL∝(p/pth)d/2 where p is physical error rate, pth≈0.57% for standard depolarizing noise; threshold condition p<pth. Stabilizer formalism uses Pauli group Pn and syndrome extraction via n ancilla qubits. | CONNECTION: Surface code lattice is a 2D square grid (crystallographic symmetry p4m wallpaper group). Error correction cycles correspond to toric code on a planar boundary — topological order with anyonic excitations. No direct golden ratio or base-60 link. | DEPTH: 8 — milestone demonstrates fault-tolerant quantum memory below threshold, a necessary condition for scalable quantum computing. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com