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 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 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 work evaluates Constraint-Level QUBO (CL-QUBO) circuit execution on IBM Quantum hardware for 34 reactor-scale fault tree subtrees spanning 5–8 basic events, selected from the 776-member quantum-feasible candidate pool established in Paper 9 (Peters, 2026). Circuits were constructed using the CL-QUBO encoding and QAOA Alternating Operator Ansatz at depth p = 1, transpiled to both ibm_torino (133 qubits) and ibm_marrakesh (156 qubits), and executed under three IBM Runtime Sampler configurations (RL0, RL1, RL2) with identical bound parameters (β = 0.2, γ = 0.2) across all resilience levels. A total of six hardware jobs consumed 476 seconds (7.93 minutes) of quantum runtime across 8,192 shots per circuit. The results demonstrate that at this circuit scale, 10–16 logical qubits transpiling to 78–150 two-qubit gates at depths of 165–338, hardware output distributions are overwhelmingly noise-dominated by standard distributional metrics. Neither RL1 nor RL2 produces improvement; RL1 actively degrades output quality relative to RL0 on both backends. However, direct comparison of hardware top-probability outcomes against SCRAM-validated prime implicants from Paper 9 reveals substantial above-random minimal cut set (MCS) enrichment relative to the uniform-random baseline in the n = 8 groups (6.2× and 3.8× expected random rate in the top-200 outcomes), demonstrating that CL-QUBO circuits produce detectable MCS-correlated structure even in distributions that appear noise-dominated by aggregate metrics. These findings characterize the current observed performance ceiling for this CL-QUBO configuration on the tested IBM backends and provide both distributional and MCS-correctness baselines against which future hardware improvements can be measured.
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.
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 reproducible, audit-ready quantum hardware experiment for minimal cut set (MCS) inference on reactor fault tree subtrees using IBM Quantum hardware. Eight two-qubit subtrees were selected from a Phase 2B reactor-scale fault tree corpus spanning four nuclear hazard families (EQK, LOCA, TRANS, SGTR). Classical baselines were computed with SCRAM 0.16.2, yielding two order-1 MCS per instance. Shallow p = 1 Quantum Alternating Operator Ansatz (QAOA+) circuits were constructed using the Constraint-Level QUBO (CL-QUBO) encoding from Peters (2026b), transpiled to ibm_torino, and executed as batch jobs totaling 57,344 shots across four experiments. Raw hardware results showed apparent infeasible leakage of 13.2% on the hw = 0 state. A dedicated four-circuit readout calibration protocol revealed that this leakage was almost entirely attributable to readout error: after confusion-matrix correction (κ = 1.56), the baseline experiment concentrates 98.3% of probability mass on the MCS manifold (hw = 1), with residual infeasible mass of 0.5%. Extension experiments substantially broaden the empirical foundation. Cross-pair validation on three additional qubit pairs (ibm_torino pairs 61–62 and 36–48; ibm_marrakesh pair 6–7) confirms that corrected MCS fidelity exceeds 95% across all tested physical qubit assignments (range 95.2%–98.3%), with the best pairs achieving 98.2% (ibm_marrakesh 6–7) and 97.9% (ibm_torino 61–62). A QAOA depth sweep (p ∈ {1, 2, 3}) on ibm_torino pair 0–1 quantifies noise–depth tradeoffs: because the mixer is inactive (β = 0), the observed redistribution from hw = 2 toward hw = 1 (7.8% at p = 1, 25.6% at p = 2, 40.9% at p = 3) is attributable to gate noise rather than productive mixing, with infeasible hw = 0 mass rising concurrently from 0.2% to 7.2%. A three-qubit exactly-one constraint experiment on ibm_torino triple 0–1–2 achieves 92.2% corrected hw = 1 mass from a feasible initialization, demonstrating that the CL-QUBO framework extends beyond two-qubit OR gates to richer constraint types on quantum hardware. Hard-start initialization experiments confirm that p = 1 circuits do not induce sufficient mixing to recover MCS states from non-minimal initializations, with this conclusion stable across independent replication and all tested qubit pairs. Complete experimental artifacts with SHA-256 verification are provided under the DOI above.
This paper presents a migration case study: moving quantum-identified minimal cut set (MCS) recovery processing from ad hoc research scripts into a structured package within the OpenPRA monorepo, and validating the migration on an executed-only cohort of three reactor-scale fault tree subtrees from a Generic Pressurized Water Reactor model. Two of the three validated cases (0698, 1037) share the same structural subtree (root gate G:G348, topology Class A, 5 basic events) and achieved tier 1 exact hardware recovery (3/3 reference MCS each). The third case (0905, topology Class C, 8 basic events) achieved union sensitivity recovery (tier 1: 3/4, union: 4/4) with operator attention required. The package-based workflow achieved semantic parity with the accepted legacy script-era artifacts on all three cases, preserving recovery outcomes and recommendations while producing a cleaner package-native output format. The paper defines and defends semantic parity as the appropriate migration criterion for this workflow, establishes a traceable artifact acceptance chain, and demonstrates that executed-only filtering is necessary for a defensible validation cohort in this mixed-evidence batch to prevent conflating validated recovery evidence with unevidenced diagnostic candidates. Because the cohort comprises only two unique structural configurations, the findings are limited to migration correctness on a narrow validated set rather than broad workflow robustness. This paper does not claim quantum advantage or deployment readiness.