quantum-computing

Training-free stability metric validated on 445 qubits across 3 IBM backends — 83% error reduction

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⚡ Quantum Brief

An independent researcher developed a training-free stability metric (Φ = I×ρ - α×S) that predicts qubit degradation without machine learning or backend-specific tuning. Validated on 445 qubits across three IBM quantum processors, it achieved 83% error reduction. The metric demonstrated 94.58% correlation with T2/T1 coherence times and provided 20-day early warnings before qubit failures. All five dead qubits were correctly identified (Φ < 0) with no false positives. Performance discrimination between stable and unstable qubits ranged from 8x to 18x across circuit depths of 10-500 gates. The same formula worked identically across all three backends without recalibration. Beyond quantum systems, the metric validated on neural networks (660+ architectures), mechanical bearings, turbofan engines, and cardiac arrhythmia detection using identical thresholds and constants. All results use real hardware data with no synthetic inputs. The methodology, code, and papers are publicly available for independent verification.

Training-free stability metric validated on 445 qubits across 3 IBM backends — 83% error reduction

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I'm an independent researcher. I developed a single stability metric Φ = I×ρ - α×S that flags degrading qubits before they fail — no ML training, no per-backend tuning. Tested on ibm_fez, ibm_torino, and ibm_marrakesh: - 445 qubits analyzed, r = 0.9458 correlation with T2/T1 - 83% error reduction using Φ-based qubit selection - 8-18x discrimination between low-Φ and high-Φ qubits across 10-500 gate depths - 20 days early warning before qubit degradation - All 5 dead qubits correctly identified (Φ < 0) - Works across all 3 backends with zero recalibration Same formula also validates on neural networks (660+ architectures), mechanical bearings, turbofan engines, and cardiac arrhythmia — same threshold, same constants. All real hardware data. No synthetic. Code is public. Repo: https://github.com/Wise314/quantum-phi-validation Paper: https://doi.org/10.5281/zenodo.18522745 Cross-domain paper: https://doi.org/10.5281/zenodo.18523292 Happy to answer any questions about the methodology. submitted by /u/Intrepid-Water8672 [link] [comments]

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Training-free stability metric validated on 445 qubits across 3 IBM backends — 83% error reduction

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