Closed-Loop Phi-Based Intervention During Quantum Circuit Execution on IBM Quantum Hardware

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 (ibmfez, ibmₘarrakesh, ibmₜorino), 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 ibmfez, 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 ibmfez (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 ibmfez, and on ibmₘarrakesh 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.