HAQA: A Hardware-Guided and Fidelity-Aware Strategy for Efficient Qubit Mapping Optimization

2025-04-23

AI Summary

To address low mapping efficiency, poor fidelity, and failure in solving large-scale problems—challenges arising from limited physical connectivity and hardware imperfections in superconducting quantum processors—this paper proposes a hardware-aware quantum circuit mapping framework. Our method introduces (i) a topology-based iterative community detection strategy for mapping space dimensionality reduction, and (ii) the first fidelity-driven regional evaluation mechanism, integrating fidelity metrics deeply into the entire mapping pipeline. The framework synergistically combines graph community detection, hardware-aware heuristic search, and polynomial-time optimization algorithms. Experimental results demonstrate that, compared to Qsynth-v2 and TB-OLSQ2, our approach achieves speedups of 632.76× and 286.87×, respectively, while improving average fidelity by up to 238.28%, all without compromising solution quality.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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%

Problem

• Optimizes qubit mapping for quantum hardware connectivity constraints
• Integrates hardware fidelity metrics into mapping decisions
• Accelerates solving speed while improving circuit execution quality

Innovation

• Community-based iterative region identification strategy
• Hardware-characteristic-based region evaluation mechanism
• Fidelity-aware qubit allocation integration