Probabilistic Risk Assessment (PRA) provides a systematic approach to characterizing risk by analyzing accident scenarios, their associated likelihoods, and resulting consequences, supporting risk-informed decision making in safety-critical systems. As PRA models grow in structural complexity and parametric dimensionality, computational scalability has emerged as a central challenge. Large combinatorial state spaces, rare-event probability estimation, and high-dimensional uncertainty propagation often require extensive Monte Carlo simulation and advanced sampling techniques, resulting in substantial computational cost. Recent advances in quantum computing suggest theoretical speedups for probabilistic estimation, combinatorial search, and high-dimensional integration. However, the relevance of quantum algorithms to full-scale PRA workflows remains largely unexplored. Existing discussions typically focus on isolated sub-problems (e.g., fault tree evaluation), without analyzing them within the broader PRA methodological structure. This paper introduces a feasibility framework to systematically map core computational tasks in PRA, including top-event and rare-event probability estimation, sensitive analysis, importance measure computation, and uncertainty propagation, to potential quantum computational algorithms such as amplitude estimation and quantum search. Rather than direct quantum replacement, we propose a hybrid quantum-classical architecture in which quantum computing functions as an accelerator within existing PRA workflows. We analyze scalability thresholds, state-preparation constraints, circuit depth requirements, and current hardware limitations to identify realistic near-term and long-term opportunities. The contribution of this work is a methodological roadmap that clarifies where quantum computing may meaningfully reduce computational burden in PRA. This study provides the first system-level assessment of quantum improvement potential across the PRA workflow, offering a strategic perspective for future research at the intersection of risk analysis and advanced computation.