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.
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 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.
We analyze 10.4 million calibration measurements from three IBM Heron superconducting quantumprocessors and identify two physically distinct classes of correlated multi-qubit degradationevents using only routine cloud-accessible telemetry. A column-wise permutation test reveals statisticallysignificant excess of simultaneous T1 crashes (42.8× expected at ≥ 10 simultaneous,p < 0.0001), simultaneous T2 crashes (4.8× at ≥ 10, p < 0.001), and simultaneous CZ gate-errorspikes (23.4× at ≥ 20, p < 0.0001; excess reaching 15,000× at ≥ 30). All three signals replicateacross backends (Fisher combined p < 10−7). Classifying high-crash snapshots by which parametersdegrade reveals two event types: Type A events degrade T1, T2, and CZ gate fidelity simultaneously(Cohen’s d = −0.94 for crashed-qubit T2), consistent with quasiparticle poisoning from high-energyparticle impacts; Type B events degrade T2 alone without affecting T1 or gate errors, consistentwith correlated dephasing noise. Type B events recover more slowly than Type A, persisting overmultiple calibration cycles with the same qubits remaining affected. T1-only events are rare acrossbackends, consistent with the expectation that relaxation degradation typically accompanies dephasing.Different qubits crash each time (mean Jaccard similarity ≤ 0.04), and readout fidelityremains unaffected in both event types. These correlated events—previously detectable only withdedicated particle detectors—are identifiable, classifiable, and operationally actionable from existingcloud calibration data without hardware modification.
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 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.
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.
Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house20-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering, we identify stable and noisy qubits. We also validate our model using GHZ state experiments. Our study provides health indicators as well as hardware-driven maintenance and recalibration recommendations, thus motivating the integration of relevant schedulers with HPCQC workflows.