Abstract Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 µs to 340 µs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n = 2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 µs, compared to IBM-reported pulse-level T₂ of 32–340 µs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜ v ₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.
Abstract Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 µs to 340 µs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n = 2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 µs, compared to IBM-reported pulse-level T₂ of 32–340 µs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜ v ₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.
Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 μs to 340 μs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n=2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 μs, compared to IBM-reported pulse-level T₂ of 32–340 μs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜᵥₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.
Abstract We introduce Quantum Diagnostic Intelligence (QDI), a five-layer decision-support architecture for inferring the operational state of quantum processing units from circuit-level measurements. Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the dominant industry standard for certifying quantum hardware health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors, that circuit-level measurements contain diagnostic information that T₁/T₂ may not capture. On ibm_marrakesh between July 3 and July 5, 2026, IBM-reported T₂ improved from 303 to 340 µs (+ 12%), while circuit-level GHZ scaling yielded α = 3.811, a QDI health score of 37.8/100 [95% CI: 7.6, 67.9], and a circuit infidelity increase of 920% at n = 2 qubits. The QDI architecture formalizes this observation through four operators: a diagnostic state classifier D(χ), a health score H(χ), a recommendation generator R(χ), and a drift forecast F(χ, t). Rules trained on ibm_kingston correctly classified ibm_fez and both ibm_marrakesh sessions without retuning (3/3 held-out tests; n = 4 total — feasibility demonstration only). We identify three scientific limitations of the current framework: operators rely on expert-designed rules rather than learned statistical models; ground truth (algorithm success/failure) has not yet been collected; and diagnostic state labels have not been validated by clustering analysis. Prospective validation on n ≥ 50 independent sessions is required before statistical claims about generalization can be made. The dataset, code, and QDI system are openly available.
Abstract We introduce the K–R scaling exponent α, extracted from the three-parameter cost decomposition C = C₀/Kᵅ + R, as a compact diagnostic descriptor of error-accumulation regimes on noisy intermediate-scale quantum (NISQ) hardware. Here, C is the measured circuit infidelity, K is a structural parameter (inverse depth or inverse qubit count), C₀ is the correctable error amplitude, R is the irreducible noise floor, and α quantifies the rate of error accumulation. Through three experimental campaigns totaling approximately 150 circuit executions on two 156-qubit IBM Quantum Heron r2 processors (ibm_kingston and ibm_fez), we observe three distinct scaling regimes: (i) structured scaling with super-linear α in GHZ circuits (α = 1.54–1.64, R² > 0.97) and noise amplification (α = 1.19, R² = 1.000), consistent with correlated multi-qubit errors; (ii) controlled stochastic scaling in Clifford depth and echo circuits (α ≈ 0.5–0.7, R² up to 0.998); and (iii) saturation where α → 0. We organize these results in an (α, R²) phase diagram—the first such empirical diagram for quantum hardware noise—and demonstrate that α serves as a low-cost diagnostic observable enabling regime identification, device fingerprinting, and stability monitoring using fewer than 13 circuits per device. An honest comparison shows that for depth extrapolation, exponential models outperform K–R; however, K–R uniquely provides regime-level insight and device-specific behavioral signatures not accessible from exponential decay rates. By reducing quantum hardware characterization to a small set of experimentally accessible scaling parameters, the K–R framework provides a practical bridge between low-cost benchmarking and physically interpretable noise diagnostics in the NISQ era.
We present empirical evidence that a thermodynamic stability metric Φ = I × ρ − α × S serves as a leading indicator of qubit degradation on IBM Quantum hardware and provides practical discrimination across multiple circuit and hardware validation settings. Analysis of 445 qubits across three backends (ibm_fez, ibm_marrakesh, ibm_torino) over 30 days demonstrates 100% detection of IBM-reported status transitions in the tested window, with average lead time of 163 hours (6.8 days) and maximum lead time of 480 hours (20 days) for warnings occurring at least 24 hours before degradation. The coherence ratio ρ = T2/T1 accounts for 70 to 78% of component contribution in backend-split component analysis, suggesting a substantially larger role for coherence information in stability monitoring than is reflected in prior qubit-selection heuristics. Cross-backend transfer achieves 98.4% balanced accuracy without retraining. Additional validation demonstrates 4.34x higher two-qubit gate error for low-Φ qubit pairs, 8 to 18x error discrimination across circuit depths from 10 to 500 gates, and correct identification of all five failed qubits in the tested dataset via Φ < 0. These results establish Φ as an early warning indicator for quantum processor maintenance and a practical qubit selection metric. The methods described in this paper are the subject of U.S. Provisional Patent Application No. 63/952,883 (filed January 2, 2026). No license to implement or commercialize the described methods is granted by this publication. All rights reserved.
Abstract Current quantum processor monitoring relies on individual qubit metrics (T1, T2, gate fidelity, readout error), missing degradation encoded in qubit-qubit relationships. Here we apply Weighted Jaccard (WJ) similarity analysis to track correlation architecture reorganization across three IBM quantum processors (ibm_fez, ibm_torino, ibm_marrakesh; 133–156 qubits each) over 30 days of calibration data. Full pairwise qubit correlation matrices are computed at each calibration snapshot and compared between temporal windows. All three backends show statistically significant architecture reorganization (WJ = 0.968–0.974, p < 0.001, permutation test, FDR-corrected; 95% bootstrap CI width < 0.012). The qubits participating most in correlation architecture reorganization show no association with conventional degradation indicators (rho = -0.051, p = 0.530), indicating that WJ detects a class of hardware change invisible to component-level monitoring. Cross-backend comparison reveals that architectural divergence between processors (~ 8–9%) exceeds within-processor temporal drift (~ 3%), suggesting hardware-specific correlation signatures.
We report the first experimental validation of a scalable, non-invasive quantum processor diagnostic system at 64-qubit scale. The TRUE Zigzag protocol characterizes T1 and T2 coherence properties of all target qubits simultaneously within a single job execution, producing a complete qubit health map from only three job submissions. Tested on IBM Quantum's ibm_fez backend (8,192 shots), the system graded all 64 qubits from EXCELLENT to UNSTABLE with baseline-to-after stability of 0.985→0.985, confirming non-invasive operation. Central discovery — Decreasing Scaling Law: Average diagnostic disturbance measured 0.252% at 8 qubits, 0.146% at 32 qubits, and 0.148% at 64 qubits, representing 92.7% improvement over linear scaling predictions. The 64Q/32Q ratio of 1.01× demonstrates plateau behavior, indicating no accuracy loss at arbitrarily larger scales. All three IBM Quantum Job IDs are publicly disclosed for independent verification. Full per-qubit data (CSV), experimental metadata (JSON), and visualization (PNG) are included. Patent: KR 10-2025-0180416