This paper presents a bounded quantum integration capability for the OpenPRA probabilistic risk assessment (PRA) monorepo, validated across a seven-workstream project covering preparation, recovery, broader cohort validation, bounded importance measures, execution reporting, and user-facing workflows. Building on a prior three-case recovery migration case study [1], this paper extends the evidence base to a 34-case validated baseline spanning topology classes A, C, and D, supported by 120-case reference artifact validation and 120-case statevector verification. The preparation and recovery services are demonstrated through direct REST endpoint evidence on real baseline-derived cases. Bounded importance and execution reporting pass explicit acceptance criteria through 10 automated checks including 7 Jest test suites. A user-facing quantum readiness review page renders populated recovery data including primary recovery mode, per-tier recovery counts, and boundedness statements through six backend endpoints, all returning HTTP 200 with populated payloads. All seven project workstreams are closed with documented acceptance gates, holdout adjudication, and exception disposition. The integration is explicitly bounded: it does not claim quantum advantage, production readiness, or live hardware execution from the web interface. The contribution is a validated, auditable integration framework demonstrating how quantum-identified fault tree MCS recovery results can be surfaced through a structured PRA platform while preserving semantic parity with prior validated outcomes and enforcing boundedness constraints throughout the user-facing stack.
This paper presents a bounded quantum integration capability for the OpenPRA probabilistic risk assessment (PRA) monorepo, validated across a seven-workstream project covering preparation, recovery, broader cohort validation, bounded importance measures, execution reporting, and user-facing workflows. Building on a prior three-case recovery migration case study [1], this paper extends the evidence base to a 34-case validated baseline spanning topology classes A, C, and D, supported by 120-case reference artifact validation and 120-case statevector verification. The preparation and recovery services are demonstrated through direct REST endpoint evidence on real baseline-derived cases. Bounded importance and execution reporting pass explicit acceptance criteria through 10 automated checks including 7 Jest test suites. A user-facing quantum readiness review page renders populated recovery data including primary recovery mode, per-tier recovery counts, and boundedness statements through six backend endpoints, all returning HTTP 200 with populated payloads. All seven project workstreams are closed with documented acceptance gates, holdout adjudication, and exception disposition. The integration is explicitly bounded: it does not claim quantum advantage, production readiness, or live hardware execution from the web interface. The contribution is a validated, auditable integration framework demonstrating how quantum-identified fault tree MCS recovery results can be surfaced through a structured PRA platform while preserving semantic parity with prior validated outcomes and enforcing boundedness constraints throughout the user-facing stack.
This study presents a bounded, auditable quantum execution framework for probabilistic risk assessment (PRA) using OpenPRA-derived fault tree encodings and provides the first platform-integrated empirical characterization of structural scaling limits within the OpenPRA quantum execution workflow. A tri-provider architecture integrating local structural validation, IBM gate-based quantum hardware, and a D-Wave-ready interface is applied to 132 constraint-level QUBO (CL-QUBO) export artifacts retained from the OpenPRA quantum-feasible candidate pool. A systematic scaling gap analysis reveals that all 132 cases occupy exactly two qubit levels—5 qubits (23 cases, 17.4%) and 8 qubits (109 cases, 82.6%)—with zero semantically valid same-root expansions beyond 8 qubits. IBM Quantum hardware validation on ibm_marrakesh confirms bounded MCS recovery at both qubit levels, including exact hardware recovery (3/3 reference MCS) at 5 qubits and partial recovery (3/4 reference MCS) at 8 qubits, with job-level provenance for every execution. A hybrid filtering framework generates 4,692 synthetic cross-root expansion candidates, of which zero preserve semantic PRA validity; 14 selected 16-qubit candidates are validated through a deterministic structural execution validator, and three are executed on IBM hardware as stress tests (156 physical qubits after transpilation, 205–220 unique bitstrings observed per job). These results establish strong evidence that the observed 8-qubit ceiling is a property of the current CL-QUBO export pipeline applied to real reactor-scale fault trees, that scaling beyond this ceiling requires structural decomposition or alternative execution paradigms, and that the integrated OpenPRA quantum platform can execute, validate, and bundle evidence across the full pipeline with auditable provenance. No claim of quantum advantage or production PRA quantification is made.
This paper presents a topology-aware feasibility roadmap for quantum probabilistic risk assessment (PRA) under bounded study conditions. The roadmap synthesizes three experimental phases applied to constraint-level quadratic unconstrained binary optimization (CL-QUBO) encodings of nuclear fault tree structures. Phase A establishes fidelity threshold behavior for the quantum approximate optimization algorithm with feasibility-preserving mixer (QAOA+) across four topology classes—Class A (anchored at n = 5), Class B (n = 6), Class C (n = 8, variant a), and Class D (n = 8, variant b)—derived from prior structural classification work. Phase B provides measured circuit-depth scaling evidence across target sizes from 5 to 20 basic events, including a deterministic constructor amendment that closes a previously missing Class B measured family. Phase C synthesizes measured and projected requirement rows into a 28-row requirements matrix spanning four topology classes and seven target sizes, with explicit evidence-tier marking distinguishing 19 measured rows from 9 projected rows. A public hardware capability screen maps the roadmap’s required-qubit count (equal to the number of basic events n in each encoded instance) against publicly reported qubit counts for Google Willow (105 qubits) [31], IBM Heron r2 (156 qubits) [32], IonQ Forte Enterprise (36 qubits) [33], and Quantinuum H2 (56 qubits) [34]; the required-qubit count does not exceed the published qubit counts for any mapped platform. A bounded C3 utility bridge links the roadmap outputs to local PRA importance-measure behavior across Fussell–Vesely, Risk Achievement Worth, and Birnbaum importance families, with interpretation restricted to the studied setting. The roadmap identifies topology class as the dominant differentiator of threshold and scaling behavior: Classes A and C are threshold-favorable under the tested conditions, while Classes B and D are threshold-unfavorable. Pooled treatment across topology classes would mask these real structural differences. This paper does not claim quantum advantage, does not include live IBM hardware execution, and does not make deployment readiness or commercialization forecasts.
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 (ibm_marrakesh 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.
Minimal cut set (MCS) identification is central to fault-tree-based probabilistic risk assessment (PRA), but quantum MCS methods have largely remained simulation-based or require Grover-style depths that are not executable on current hardware. This study reports an end-to-end IBM Quantum validation using shallow, fixed-parameter alternating-operator constraint-level QUBO (CL-QUBO) sampling circuits. The primary campaign executed 132 full-paired raw RL0 cells across 66 SCRAM-derived reactor fault-tree subtrees on two IBM devices, with 12 mapping-replicate controls held separate. Under an independently locked little-endian decoding convention, top-200 hardware outputs produced 2,596 exact-MCS state matches against 778 expected under a matched random-projection baseline, a 3.34× enrichment with 0.901 aggregate unique-MCS recovery. A 2,000-trial matched random-state null placed the all-campaign observed count outside the null distribution (empirical p < 5.0 × 10⁻⁴). Recovered cut sets preserved local PRA importance rankings with high fidelity, with mean Spearman correlations of 0.969 for Fussell-Vesely, 0.950 for Risk Reduction Worth, 0.934 for Birnbaum, and 0.920 for Risk Achievement Worth. A post-primary, pre-registered attribution ladder showed that removing the CL-QUBO cost phase collapsed the hardware result to the random/no-cost simulation scale (top-200 ratio 0.985), while the full p = 1 circuit remained at 3.34×. The results support cost-phase attribution for exact-MCS enrichment on current hardware, while making no claim of quantum advantage, plant-level risk quantification, or uniform behavior across all subtree classes.
This paper presents a formal validation framework establishing equivalence between Quadratic Unconstrained Binary Optimization (QUBO) formulations and classical minimal cut set (MCS) identification for fault tree analysis in probabilistic risk assessment. The framework develops a 21-gate hierarchical verification system that proves QUBO ground states correspond exactly to minimal cut sets computed by the SCRAM fault tree solver. The validation methodology employs exhaustive enumeration for tractable models and conservative spot checks for larger instances, achieving PASS status across all verification gates. The theoretical contribution formalizes MCS identification as a constrained Boolean satisfiability problem amenable to quantum optimization. Fault trees with OR-of-ANDs structure are encoded into QUBO form with penalty terms enforcing logic constraints. The encoding preserves semantic equivalence: valid QUBO ground states map bijectively to classical minimal cut sets. Empirical validation demonstrates the framework on a canonical test case (TOP = A ∨ B ∨ (C ∧ D)) using QAOA simulation with plateau-based convergence methodology. The quantum circuit correctly identifies valid MCS configurations with p_opt = 0.01058 (95% CI: [0.00972, 0.01151]). Hardware feasibility is demonstrated through execution on IBM's ibm_torino 133-qubit Heron processor, where all circuits passed sanity and interpretation checks despite noise distributing probability across 110-120 unique states. The framework establishes foundational infrastructure for quantum PRA research by providing: (1) formal QUBO-MCS bijection verification against ground truth, (2) a reproducible classical baseline using the SCRAM solver, (3) plateau-based convergence criteria for stochastic quantum optimization, and (4) a hierarchical verification gate system ensuring scientific defensibility. No claim of quantum advantage is made; results are evaluated solely for semantic correctness, reproducibility, and NISQ feasibility.
This record contains a manuscript and supporting reproducibility materials for “Ledger-Valid Recovery Diagnostics for Quantum Computing: A Prespecified Sequential Audit for Claim-Limited Recovery Validation.” The paper proposes a methodological validation framework for quantum-computing recovery claims. Its central claim is that endpoint recovery success is necessary but not sufficient for a strong recovery claim when calibration failure, false-carrier acceptance, postselection artifacts, leakage, sentinel blindness, or hidden discard can carry failure modes out of view. The manuscript introduces a prespecified S0–S4 ledger audit that converts familiar diagnostic ingredients—label calibration, candidate recovery, false-carrier challenge tests, postselection/leakage accounting, and sentinel observability—into an ordered, claim-limiting taxonomy. A supportive recovery claim is permitted only when all required gates pass; otherwise, the first failed stage determines a lower claim ceiling and an interpretable failure class. The evaluation is based on synthetic endpoint-level validation suites rather than circuit-level simulation or hardware data. These suites test the behavior of the decision architecture under clean support, degraded observability, false-carrier artifacts, postselection/leakage artifacts, sentinel-blind cases, correlated artifacts, and adversarial overlap. The results show a strong specificity/sensitivity tradeoff: the full ledger protocol sharply reduces synthetic non-support supportive-call rates relative to endpoint-only reporting, but with substantial loss of mechanism-positive support sensitivity. The paper explicitly treats this as a conservative claim-validity architecture, not as a new quantum error-correcting code, new physical observable, hardware speedup, or proof of improved device performance. The supporting materials document the final synthetic validation protocol, endpoint definitions, decision-bound implementation, direction-aware threshold sensitivity analysis, latent-threshold eligibility analysis, mechanism-negative/latent-eligible boundary cases, and figure/table traceability. The archive is intended to support review of the manuscript’s aggregate numerical claims and methodological provenance. Hardware implementation, independent simulator validation, and full raw-event-ledger reproduction are identified as future extensions. Suggested keywords: quantum computing; quantum error correction; validation methodology; recovery diagnostics; leakage; postselection; benchmarking; synthetic validation; decision architecture; reproducibility.