Quantum-accelerated Bayesian network (BN) structure learning asks whether quantum algorithms can speed up the combinatorial search over directed acyclic graphs while still making realistic systems assumptions. The “realistic assumptions” angle focuses on moving beyond idealized models (e.g., unbounded fault-tolerant resources, strong qRAM access, or oracle cost-free scoring) and instead quantifying end-to-end costs: how data access is performed, how scoring functions (BIC/MDL/BDeu) are computed, what the qubit/depth requirements are, and how noise and limited connectivity affect usable speedups. A strong research direction is to hybridize: use classical heuristics to prune candidate parent sets and restrict search spaces, then apply quantum subroutines (e.g., amplitude amplification, QAOA-style optimization, or quantum-enhanced dynamic programming) on the reduced problem—reporting not just asymptotic gains but practical break-even points on problem sizes (n variables, max indegree) and resource estimates that match near-term and early fault-tolerant hardware.