Quantum algorithms rely on quantum computers for implementation, but the
physical connectivity constraints of modern quantum processors impede the
efficient realization of quantum algorithms. Qubit mapping, a critical
technology for practical quantum computing applications, directly determines
the execution efficiency and feasibility of algorithms on superconducting
quantum processors. Existing mapping methods overlook intractable quantum
hardware fidelity characteristics, reducing circuit execution quality. They
also exhibit prolonged solving times or even failure to complete when handling
large-scale quantum architectures, compromising efficiency. To address these
challenges, we propose a novel qubit mapping method HAQA. HAQA first introduces
a community-based iterative region identification strategy leveraging hardware
connection topology, achieving effective dimensionality reduction of mapping
space. This strategy avoids global search procedures, with complexity analysis
demonstrating quadratic polynomial-level acceleration. Furthermore, HAQA
implements a hardware-characteristic-based region evaluation mechanism,
enabling quantitative selection of mapping regions based on fidelity metrics.
This approach effectively integrates hardware fidelity information into the
mapping process, enabling fidelity-aware qubit allocation. Experimental results
demonstrate that HAQA significantly improves solving speed and fidelity while
ensuring solution quality. When applied to state-of-the-art quantum mapping
techniques Qsynth-v2 and TB-OLSQ2, HAQA achieves acceleration ratios of 632.76
and 286.87 respectively, while improving fidelity by up to 52.69% and 238.28%

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