A Framework for Dynamic Quantum Circuit Execution: Balancing Effectiveness and Efficiency

Abstract

Quantum computing has exhibited remarkable advancements in recent years. On superconducting quantum chips, physical qubits are interconnected with limited coupling topologies. Since two-qubit gates can only be operated between adjacent qubits, quantum circuits must undergo transformation to satisfy these connectivity constraints—a process known as qubit mapping and routing. Dynamic quantum circuits represent a critical paradigm in quantum computing, which features mid-circuit measurements and conditional control flows (controlled subcircuits) based on measurement outcomes. A distinctive challenge arises when controlled subcircuits are determined only after measurement results become available (online acquisition). Thus, mapping and routing dynamic quantum circuits presents complex challenges, including managing the conditional execution of controlled subcircuits, accommodating their online/offline acquisitions, and resolving mapping misalignments between circuit segments. In this work, we propose a comprehensive framework for transforming and executing dynamic quantum circuits. In this framework, we present a mapping and routing algorithm Sword that effectively reduces the quantum processing time of quantum circuits, which applies SWAP(s) with occupancy and gate regional density. For online scenarios, we introduce a fast variant Sword-fast that significantly improves transformation efficiency with negligible performance degradation. Experimental evaluations demonstrate that our framework achieves an average 37.1% reduction in end-to-end wall-clock time compared with baseline approaches for offline scenarios. In online scenarios, our framework achieves 59.2% average reduction of end-to-end wall-clock time, demonstrating $2.6\times $ average speedup in transformation time and 30.0% average reduction in quantum processing time. By replacing the qubit mapping and routing algorithm in our framework with a fast variant, Sword-fast, we achieve a $9.6\times $ average speedup in transformation time compared with the standard version.