DAG-Aware Gate Fusion for Efficient State-Vector Quantum-Circuit Simulation

Abstract

Classical simulation remains a practical foundation for developing and evaluating quantum circuits, and state-vector methods are still among the most important exact simulation techniques. In this setting, gate fusion is a widely used optimization that combines multiple gates into a larger operator to reduce repeated state-vector traversal and improve execution efficiency. However, existing fusion methods are often driven by local heuristics or linear gate order, which can miss larger legal fusion opportunities allowed by circuit dependencies. To address this issue, we propose a directed acyclic graph-aware gate fusion method that builds legal fused operators directly from the circuit dependence graph through topological expansion, a configurable fusion-size bound, and a nonblocking rule. Using the pyquafu state-vector simulator as the execution backend, the method delivers strong end-to-end performance on representative benchmarks and yields high compression ratios. The results also highlight that fusion quality alone is not sufficient: the realized speedup depends on how well the fused blocks match the execution characteristics of the simulator backend.