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.
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.
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 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 present an emulator-based and hardware feasibility study of Aurora-DD, a phase-coherence compensation method that integrates a sign-based feedback update of a global phase offset (Delta phi) with a fixed-depth XY8 dynamical decoupling (DD) scaffold. The feedback optimization is performed offline on a calibrated emulator and the resulting Delta phi* is deployed as pre-calibrated phase compensation on hardware. This represents an "offline closed-loop, online open-loop" feasibility demonstration. Using an Aer-based emulator calibrated with ibm_fez device parameters, Aurora-DD achieves substantial reductions in mean-squared error of the measured expectation value <Z>, yielding 68-97% improvement across phase settings phi = 0.05, 0.10, 0.15, 0.20 over n=30 randomized trials. These large-n emulator results provide statistically stable evidence that the combined effect of XY8 and Delta phi* suppresses both dephasing and systematic phase bias. On real superconducting hardware (ibm_fez), we perform a small-sample (n=3) multi-phase validation campaign. Aurora-DD yields point estimates corresponding to approximately 99.2-99.6% reduction in absolute error relative to a no-DD baseline across all tested phase points. These hardware numbers are reported transparently as feasibility evidence under tight queue and credit constraints. In contrast, the auxiliary Aurora+ZNE branch exhibits instability: shallow two-point ZNE occasionally amplifies calibration inconsistencies and produces large error outliers. We therefore relegate ZNE analysis to the Appendix and position Aurora-DD (without ZNE) as the primary contribution. Overall, the combined results support pre-calibrated Aurora-DD as a practical, stable, and hardware-compatible phase-coherence compensator for NISQ devices in single-qubit settings.
This preprint reports a preregistered dynamic-circuit quantum-control experiment comparing monitored feedback against matched open-loop control for a four-qubit Kuramoto--XY synchronisation payload. The experiment uses three system qubits, one monitor qubit, and three dynamic rounds to test feedback directly on IBM gate-model hardware. The primary ibm_kingston binary endpoint is negative: the monitored-feedback arm does not improve the selected target relative to the matched open-loop control. The same S1--S1f stack is then replicated on ibm_fez and ibm_marrakesh, where the primary endpoint is positive but direct-observable extensions show backend-dependent YYI and response structure. The contribution is therefore a hardware-control boundary result: it documents where the monitored-feedback policy fails, where it appears to improve the selected target, and why the result should not be interpreted as a simple backend-independent controller promotion. The record includes the source, bibliography, and rebuilt PDF.
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.