What question did this study set out to answer?

The study aims to evaluate if quantum-identified minimal cut sets maintain probabilistic risk assessment importance measures compared to classical methods.

March 17, 2026Open Access

Quantum-Identified Minimal Cut Sets for Quantitative PRA: Importance Measures and Risk Metric Validation Against Classical Baselines for Reactor-Scale Fault Tree Subtrees

Key Points

The study aims to evaluate if quantum-identified minimal cut sets maintain probabilistic risk assessment importance measures compared to classical methods.
Utilized frozen CL-QUBO hardware outputs for MCS recovery.
Evaluated through two probability-assignment tracks: direct-component and common cause failure extension.
Analyzed 21 representative subtrees, promoting 16 into quantitative analysis.
Employs original component unreliability data and common cause failure updates.
Primary track achieved mean recovery fraction of 0.225 with mean absolute error of 4.52 × 10⁻⁷.
Extension track achieved mean recovery fraction of 0.292 with multiple Spearman correlations indicating relationships.
Tighter extraction windows generally outperformed wider ones; set Jaccard proved more useful for downstream applications.

Abstract

This paper presents the first end-to-end evaluation of whether quantum-identified minimal cut sets (MCS) from physical IBM Quantum hardware can preserve standard probabilistic risk assessment (PRA) importance measures when compared against frozen SCRAM-validated classical baselines. It consumes frozen CL-QUBO hardware outputs from Paper 10 and propagates recovered MCS through two defensible probability-assignment tracks: a primary direct-component track based on NUREG/CR-6928 original component unreliability data and a secondary CCF extension track based on the 2020 NRC/INL common cause failure update using alpha factor methodology. Of 21 selected representative subtrees, 16 are promoted into the formal quantitative analysis set (10 primary, 6 extension), while five additional subtrees remain executed but quarantined in exploratory holdout lanes. Exact MCS recovery remains incomplete across both tracks. At the shared controlled operating point (ibmₘarrakesh RL1 top-k = 10), the primary track achieves mean recovery fraction 0. 225 with mean top-event absolute error 4. 52 × 10⁻⁷, while the extension track achieves mean recovery fraction 0. 292 with mean RAW Spearman 0. 488, Birnbaum Spearman 0. 438, FV Spearman 0. 236, and mean top-event absolute error 7. 50 × 10⁻⁷. Across the promoted set, tighter extraction windows generally outperform wider windows, set Jaccard is more informative than simple recovery fraction for downstream utility within this study, and the extension-track advantage survives common-support restriction but remains conditional and metric-dependent. The resulting pipeline therefore shows measurable but limited PRA utility: enough for screening-level assessment of relative component significance under favorable conditions, but not enough for standalone regulatory-grade quantification.

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