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 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 investigate whether the three normalized components of a thermodynamic stability metric, normalized fidelity I, coherence ratio ρ = T2/T1, and readout error S, can serve as training-free input features for machine learning models that predict the Φ-threshold qubit-quality class across IBM Quantum hardware. Using a strict methodology that excludes Φ from the feature set and evaluates generalization via backend-split evaluation, we find that on 445 qubits from three IBM Quantum backends, (I, ρ, S) predict the Φ-threshold class with 98.4% average balanced accuracy on held-out backends without retraining. Feature ablation reveals that ρ alone achieves 94.6% balanced accuracy and carries 70 to 78% of feature importance, while I alone and S alone are near random at 52.2% and 50.9% respectively. All four tested model architectures (Random Forest, Gradient Boosting, Neural Network, SVM) exceed 80% balanced accuracy under the strict protocol. For the narrower task of predicting T2-based hardware quality, a data-driven threshold of 0.60 outperforms the physics-derived threshold of 0.25 by 16.2 percentage points. These results establish (I, ρ, S) as a model-agnostic, training-free feature representation for predicting the Φ-threshold qubit-quality class across the tested IBM Quantum backends, with coherence ratio ρ carrying most of the predictive signal. The methods described in this paper are the subject of U.S. Provisional Patent Application No. 63/956,800 (filed January 9, 2026). No license to implement or commercialize the described methods is granted by this publication. All rights reserved.
We investigate whether a training-free thermodynamic stability metric Φ = I × ρ - α × S, computed from IBM Quantum calibration data before circuit execution, can serve as a routing signal for allocating computation between quantum hardware and classical simulation in the tested setting. Across 23 tests on real IBM Quantum hardware, we validate a Φ-based routing framework and show in direct crossover tests that LOW-Φ quantum execution can be worse than exact classical simulation for the tested classically tractable circuits: in the primary crossover test, classical simulation achieved 0.00% error while LOW-Φ quantum execution produced 8.23% error on the same circuit. Under a strict methodology with verified physical qubit binding and decisive ground truth, HIGH-Φ versus LOW-Φ error ratios ranged from 2.90x (QPE) to 30.47x (Bernstein-Vazirani) across 7 tested algorithms. The strict seven-algorithm tests constitute the primary evidence layer, while the broader 23-test suite provides supporting boundary conditions on size, depth, and reproducibility. Circuit size and depth impose additional routing boundaries: in the tested conditions, LOW-Φ execution at 15 qubits produced 94.85% error and execution at circuit depth 103 produced 67.29% error, both transitions into regimes where classical fallback was more reliable. These results support a routing decision framework in which minimum Φ across circuit qubits determines whether to execute on quantum hardware or fall back to classical simulation, with hybrid strategies retained as a natural extension. The methods described in this paper are the subject of U.S. Provisional Patent Application No. 63/956,752 (filed January 9, 2026). No license to implement or commercialize the described methods is granted by this publication. All rights reserved.
We investigate whether a training-free thermodynamic stability metric Φ = I × ρ - α × S, computed from IBM Quantum calibration or telemetry data, can drive closed-loop intervention decisions during execution-associated monitoring windows on real hardware. Across 10 paid circuit tests on three IBM backends (ibm_fez, ibm_marrakesh, ibm_torino), together with simulated and telemetry-based harness validation, we evaluate a closed-loop controller that monitors Φ during an execution-associated window and supports five intervention actions: CONTINUE, CONTINUE-DEGRADED, CLASSICAL-FALLBACK, CHECKPOINT-MIGRATE, and ABORT-RESTART. In a same-circuit controlled comparison on ibm_fez, swapping LOW-Φ qubits (Φ = 0.097, 0.030) for HIGH-Φ qubits (Φ = 0.9988, 0.9984) reduced error from 18.36% to 2.73% on an identical Deutsch-Jozsa circuit, an 85.1% relative error reduction. Under matched conditions, composite Φ selection outperformed raw T2 selection by 7.79 percentage points on ibm_fez (68.7% relative reduction) and by 6.37 percentage points aggregate across three backends. Mid-circuit dynamic branching on IBM hardware achieved 98.78% per-shot conditional consistency between probe outcome and branch marker, with an estimated 101,220 logical gates avoided on the ABORT path. A Wilson confidence interval non-overlap rule triggered ABORT-RESTART on 1 of 3 backends in the tested setting. Classical checkpoint representation at a segmentation boundary enabled suffix migration from LOW-Φ to HIGH-Φ qubits with 82.15% final fidelity, without storing an unknown quantum state. Honest counterexamples are preserved: in a three-policy cost comparison a seeded random pair outperformed the Φ-guided pair on ibm_fez, and on ibm_marrakesh raw T2 selection outperformed Φ selection under matched compiled circuit conditions. These results support a closed-loop intervention framework in which Φ can operate as a control variable during execution-associated monitoring rather than only as a pre-execution selection score. The methods described in this paper are the subject of U.S. Provisional Patent Application No. 63/973,723 (filed February 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.
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 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.