Hardware companion to "Gleason from Inside". Three sessions on ibm_kingston (554 of 600 free-tier seconds): the interior Born audit ran end to end, self-calibrated, certified positivity live at the lattice boundary, and kept its readout thermometer clean (claim A); the quantitative Born bound was not issued, exactly as pre-committed, because its noise-model condition failed; and that failure is the finding: schedule-accumulated coherent crosstalk on one coupler, detected by the cross-context identity, localized by zero-write baselines, converted into a gauge by schedule harmonization (five-fold collapse of the violation), and tracked in time (the phase flips sign within a single calibration epoch; coherence time 3 to 60 minutes; our own recalibration attribution falsified and corrected). Detect, localize, discriminate, track, on 9.2 minutes of free-tier time. The discipline (hash-frozen analysis, pre-committed outcome tables, credential lint extended from topology into time) is documented as a result in itself.
Hardware companion to "Gleason from Inside". Three sessions on ibm_kingston (554 of 600 free-tier seconds): the interior Born audit ran end to end, self-calibrated, certified positivity live at the lattice boundary, and kept its readout thermometer clean (claim A); the quantitative Born bound was not issued, exactly as pre-committed, because its noise-model condition failed; and that failure is the finding: schedule-accumulated coherent crosstalk on one coupler, detected by the cross-context identity, localized by zero-write baselines, converted into a gauge by schedule harmonization (five-fold collapse of the violation), and tracked in time (the phase flips sign within a single calibration epoch; coherence time 3 to 60 minutes; our own recalibration attribution falsified and corrected). Detect, localize, discriminate, track, on 9.2 minutes of free-tier time. The discipline (hash-frozen analysis, pre-committed outcome tables, credential lint extended from topology into time) is documented as a result in itself.
An interior observer cannot step outside its world to test the quantum probability rule; everything it knows arrives through records written into its own memory. This paper asks what such an observer can certify about the rule from those records alone, within a minimal two-record collision register. Results: the register's complete memory-side statistics (census) in closed form, making it a self-calibrating interior tomograph; an interior Gleason theorem at first order (the audit's response matrix has full rank with kernel exactly the Born-model tangent; the threshold is two records); the global forgery geography (a bounded shell and a lattice stratum pass every identity) with an identity-futility theorem proving this necessary and an exact repair (one PSD condition on a back-action-corrected moment matrix, complete at two records with gauge classified); the completed arena (det M_c = sin^2 phi (1 - |b|^2): one complex PSD condition is physicality plus tomography, its imaginary part the register's measurable complexness); the real-amplitude strike (complexness is credential-relative: cell dimension and readout sharpness; with both enforced, real registers are convicted by the census's zero channels at a dial rate of 0.40 census gap per unit certified sharpness); and a hardware campaign on ibm_kingston in which the audit detected, localized, mechanism-confirmed, and time-tracked a device systematic. Pre-registered throughout; failed predictions ledgered in an appendix.
Note: description expanded on 2026-10-14 for readability. It adds no new predictions or criteria. The frozen pre-registration is the attached PDF, unchanged since publication. Status: the experiment was run on 2026-10-14, after this record was published. Results, including every pre-registered test, will be reported in a separate record linked here. In plain language Theory says a quantum system never completely forgets where it started, even when its dynamics are chaotic. This leftover memory is called a quantum birthmark (Graf et al., Phys. Rev. X, 2026). Theory also says the birthmark should be larger when the system's rules run the same forwards and backwards in time (time-reversal symmetry) than when that symmetry is broken. This record pre-registers a test of that prediction on IBM's ibm_kingston quantum processor. We built two nearly identical sets of 4-qubit circuits. One set has time-reversal symmetry and the other does not. Both use exactly the same gates, so they experience the same hardware noise. We measure how often each circuit returns to its starting state, compared with the 1-in-16 chance level, and predict that the symmetric set shows about 1.67× more memory than the broken set. All predictions, pass/fail criteria, circuits and analysis code were deposited here before any quantum hardware was used. What is tested, and what is not Tested: whether the return probability stays above the ergodic baseline 1/16 at depths 8–23, and whether that excess is larger for the time-reversal-symmetric class by the ratio exact numerics predict for these fixed instances. Not tested: the withdrawn temporal-memory claims of 10.5281/zenodo.20470129. The birthmark is closed-system coherent memory, which measurement and reset erase, so no outcome here bears on those claims. Not tested: the universal random-matrix value 1.889. At this size (N = 16) the circuit family itself gives about 1.66, so a finite-size difference is expected. Not a quantum-advantage claim: a 16-dimensional system is exactly simulable. This is a hardware-validation experiment. Design Hardware: ibm_kingston, physical qubits 108–109–118–129 as an open chain, selected by a no-QPU calibration audit (included as v6_backend_audit.zip). Circuits: a kicked-Ising Floquet period Kx(a/2) → D(h1) → Kx(b) → D(h2) → Kx(a/2), with D(h) = exp(−iΣhiZi)·exp(−iπ/4 ΣZiZj), at exactly 6 CZ per period. COE (symmetric): h1 = h2. TRSB (broken): h1 = h + d, h2 = h − d. The classes differ only in virtual-Z angles. Instances: 16 seeded COE/TRSB pairs (seed 20261013), run at every depth from 8 to 23 (48–138 CZ). Observable: P(t) = |⟨0000|Ft|0000⟩|². The primary statistic is the readout-mitigated excess ratio R = ECOE / ETRSB, where E is the mean of P(t) − 1/16. A noise fidelity common to both classes cancels in R. Controls: 32 deep circuits (depths 60–63) as a decoherence-floor check. 8 state-preparation monitors. 16 readout-calibration circuits. Run: one SamplerV2 job of 568 circuits at 256 shots each, with dynamical decoupling and twirling off and the execution order shuffled. Predictions Quantity COE TRSB R Excess over 1/16, depths 8–23 (noiseless, primary) 0.0896 0.0537 1.667 Same instances, infinite-time limit 0.0952 0.0634 1.502 Circuit family, 10,000 random draws — — 1.661 Random-matrix reference, N = 16 — — 1.889 Under four simulated noise models (nominal, pessimistic, and each with coherent errors), the mitigated R stays between 1.64 and 1.77. The shot-noise standard deviation of R at 256 shots is 0.070–0.125. Pre-registered decision criteria Test Pass condition H1 (primary): symmetry dependence 99% CI lower bound of R > 1.0 H2 (primary): quantitative agreement 99% CI of R intersects 1.54–1.87 and is at most 1.0 wide S1: birthmark above decohered floor Window excess minus depth 60–63 excess, 99% CI lower bound > 0, for both classes S2: matched fidelity between classes 0.85 ≤ FCOE/FTRSB ≤ 1.15 S3: instance-level agreement Pearson r ≥ 0.80 against exact predictions S4 (flag only): state prep and drift Monitors ≥ 0.95, drift ≤ 0.045 Overall verdict: CONFIRMED if H1, H2 and S2 all pass; PARTIAL if H1 passes but H2 or S2 fails; NOT CONFIRMED if H1 fails. Every test is reported whatever the outcome. Uncertainty comes from a 10,000-sample parametric multinomial bootstrap over every circuit's counts, with instances held fixed. Amendment 1 (v1.1.0, made before any data) Amendment 1 changed two things relative to v1.0.0 (10.5281/zenodo.23355804): Shots: reduced from 1024 to 256 per circuit for QPU budget, cutting estimated execution from about 152 s to about 38 s. H2: changed from "point estimate in 1.54–1.87" to the CI rule above, because at 256 shots the point rule would falsely fail 20–25% of the time under the pessimistic models. A CI-width cap of 1.0 keeps H2 informative. Circuits, instances and predictions are unchanged. The trade-off is that H2 is now a weaker test. Provenance Every reported number is derived by script from raw counts: Source and transpiled circuits are saved as QPY. Before submission, every transpiled circuit is mapped back to 4 qubits and its unitary checked against the exact model to < 10−8. The analysis reads its decision bands only from predictions.json and its data only from the raw results file. Submission is gated on the DOI of this record. Files: The PDF is the authoritative pre-registration. predictions.json holds every predicted number and decision band. birthmark_prereg_v1.1.0.zip contains the circuit builder, verification, submission and frozen analysis scripts, plus simulated-data self-tests. v6_backend_audit.zip is the pre-submission calibration audit. Known limitations Small system size (N = 16), so the random-matrix value is not expected. The prediction is specific to depths 8–23 and to these 16 fixed instances. The noise models exclude leakage, TLS fluctuations and crosstalk from outside the chain. T1 relaxation biases P(0000) upward, which pulls R toward 1 (conservative). One backend, one path, one job: no replication. Data: CC BY 4.0. Code: MIT. Principal investigator: Amit Brahmbhatt, Quantum-Clarity LLC.
Cryogenic control electronics accumulate systematic, non-zero-mean bias — from thermal cycling of mounts, flux leakage, and dielectric memory — that a feedback loop integrates until its actuator authority is exhausted and the integrator saturates. A controller that alarms on amplitude thresholds sees this failure only after correction is no longer available. This paper reports a precursor estimator that reads the dynamics of the control signal for the onset of systematic drift, built deliberately as a finite observer in the sense of the first Finite Cognition paper: a declared error budget, an explicit competence boundary, and fail-closed abstention. Its evidence is presented in two declared strata. First, a fresh, fully archived Monte-Carlo run of record on a declared control-loop model: across 30 seeds × 7 sensor-environment conditions × two drift polarities, with every threshold frozen before the batch, the estimator detected drift before saturation in 246/354 eligible collapse runs (69.5%, 95% interval 64.5–74.1%), with per-condition detection ranging from 25.5% (all disturbances combined) to 100% (dropout; 92.4–100), median leads of 7–17 s, and zero false interdicts across 210 healthy runs within the declared calibration window (0.0–1.8%) — a bound the long-horizon batch shows is conditional on the mandated recalibration schedule. Second, an archived read of live IBM quantum-hardware calibration telemetry (a 156-qubit Heron device, June 2026): the same governed detector family flagged the two genuinely degraded qubits and the sole sustained relaxation-time decline within that window (T_1 falling 88.0 → 19.1 µs across 43 returned calibration snapshots spanning 42 calendar days) — an event a longer archived record later resolves into the first, longest and deepest of four recurring excursions on the same qubit, at a cost to this detector family the paper states while staying silent on a bad-but-stable qubit and the 154 others. The estimator holds advisory-only authority under the programme's interface contract: it emits verdicts and writes nothing to the plant. This record supersedes an earlier version whose simulation table could not be traced to a quantum-specific run; the run of record reported here, code and data archived, is the evidence this paper stands on.
Cryogenic control electronics for quantum hardware accumulate systematic, non-zero-mean bias that integrates in a feedback loop until the actuator saturates; a threshold controller detects this only after saturation. We present a precursor estimator that detects the onset of systematic drift from the dynamics of the control signal, built as a finite observer (declared error budget, explicit competence boundary, fail-closed abstention) in the sense of a companion paper on the physical limits of finite observation. It uses path-functional invariants (a Fisher–Rao metric stability bound, a maximum-likelihood bias decoder, a cumulative-damage functional) rather than eigenvalue analysis, which we show is neither necessary nor sufficient for finite-time survival. In simulation, across 30 seeds × 7 sensor-environment conditions (including a 1/f dephasing spectrum), it detects 96.7% of drift-collapse scenarios before saturation at a 12 s median lead with 0% false positives over 210 healthy runs. On real data, the same governed outlier-and-drift detector, applied to live publicly accessible IBM quantum-hardware calibration time-series (a 156-qubit IBM Heron device), flags genuinely degraded qubits and catches a real relaxation-time decline (one qubit's T1 falling 88.0→19.1 µs, −78%, over 43 days) while staying silent on a qubit that is poor but stable and on the other 155 — detection of change, not of mere badness. The work is TRL 4: an advisory detection layer, not a deployed controller, and it does not emulate entanglement, non-local correlation, or contextuality. The contribution is the integrated, calibrated estimator and its operating discipline, demonstrated in simulation and against real hardware-calibration data.
Purpose and background. Variational quantum machine-learning runs fail silently: barren plateaus, shot starvation, and hardware noise all present as a loss that stops improving. The HilbertBench instrument records circuit executions non-intrusively into sealed traces and attributes such failures offline. Our first registered study (OSF 10.17605/OSF.IO/2PWHU) validated the human-plus-instrument workflow at ceiling (72/72 blinded diagnoses) on a corpus in which each run expressed one dominant failure mechanism by construction. A ceiling result on a corpus designed to be diagnosable, however, cannot locate the instrument's limits. This study measures those limits and asks a sharper question: as a planted failure mechanism is weakened from strongly expressed to physically absent, where does detection fail, and does a human diagnostician reading the full trace evidence outperform a frozen mechanical decision rule precisely in that transition region? Design. An independent diagnostician (Researcher B from the first study, who has never seen any run in this corpus, its composition, or its answer key) diagnoses 63 blinded, simulator-generated runs from their traces alone, assigning each a primary label, an optional secondary label, a confidence, and free-text notes. The runs are produced by the byte-identical generators of the first study and span graded mechanism strengths that bracket each detection boundary, plus two-mechanism mixtures and mechanism-free controls. The class balance, per-run composition, knob levels, seeds, and generation order are withheld from the diagnostician until unsealing; the full run-level assignment is committed by SHA-256 and revealed at unsealing. Because this registration itself specifies the composition and grid, it is embargoed until the diagnosis is complete and unsealed, so that its contents cannot reach the diagnostician before she finishes her single blinded pass; the registration timestamp and the SHA-256 commitments establish that the design was fixed in advance regardless of the embargo. The comparator is a frozen mechanical rule (the naive reading of the analyzer verdicts, with a fixed mechanism precedence) whose outputs on the blinded corpus are hash-committed before the diagnostician receives the corpus, so the comparator cannot react to her results. Design rationale (simulator-only). Locating a detection boundary requires planting exactly one mechanism at an exactly known strength per run. On real NISQ hardware, single mechanisms cannot be planted cleanly -- the circuit depth that makes a landscape barren also collapses fidelity, a co-occurrence we registered and confirmed in the first study -- so hardware would introduce an uncontrolled second mechanism into every run and invalidate the answer key. This follows the standard two-arm structure of diagnostic validation: real-device (clinical) validity was established in the blinded hardware stratum of the first study; the present study is the analytical arm (limit of detection), performed on controlled references by design. The noise arm still uses calibrated device noise models with ISA transpilation, so device physics remains represented while ground truth stays reproducible. Primary outcome. A paired, one-sided exact McNemar test comparing the human's and the rule's primary labels against the planted ground truth, over the discordant runs (H1: the human is correct on more discordant runs than the rule). The paired design is used because in the transition region the knob-defined label and the trace evidence genuinely diverge; both graders face the identical ambiguity on the identical runs, and the pairing cancels it. Secondary outcomes and predictions. (H2) The human recovers the specific rule-failure classes identified previously -- borderline-SNR runs the rule grades "marginal" rather than "dominated," and shallow converged trajectories whose low variance mimics a plateau -- at a higher rate than the rule. (H3) The human's reported confidence is lower inside the transition regions than on strongly expressed anchor runs. We also report human-versus-rule detection-rate curves by planted strength, two-mechanism flag-set recall, and false positives on mechanism-free runs. Analysis and integrity. Diagnosis is a single pass. The scoring program is committed in code before any run exists; no scoring choice is made after data are collected. The diagnostician's filled sheet is hash-committed before the answer key, the corpus composition, and the rule outputs are unsealed. All commitments are auditable: repository commit b35ee2b (protocol and scorer), assignment hash 540dfefccdf1d33312a7ab3711c2486040ace46c86c2ef312fb1b1e03f35b08a, answer-key hash e7dbf167da9a7dc33219e7721a697c28b2abefa6a76a85c5bf3725417baa0139, and rule-outputs hash 6da0c442e2ca143916921f162a6a3e01f90e0fca46964bbc22dd9e5d70a12c49. Status at registration. The corpus has been generated and blinded and the comparator outputs committed; the diagnostician has not yet received the corpus, and no outcome data exist.