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

TL;DR

HAQA presents a hardware-guided, fidelity-aware strategy to optimize qubit mapping by partitioning the coupling graph into regions via Recursive Community Fusion and then expanding regions to provide sufficient ancilla qubits. By integrating hardware topology and two-qubit gate fidelity into region selection, HAQA reduces the solver's effective search space, yielding polynomial-time acceleration and fidelity improvements when paired with existing solvers. Empirical results on IBM Eagle and Heron show substantial speedups (up to hundreds of times faster) with notable fidelity gains, while maintaining comparable circuit depth and swap counts. The work demonstrates that region-based, hardware-aware mapping is a practical and scalable path to improving quantum circuit realization on NISQ devices.

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%

Figures (5)

• Figure 1: The depiction of qubit mapping.
• Figure 2: Recursive Community Fusion process on IBM QX2 coupling graph, each step illustrates community mergers (indicated by arrows) based on the reward function F. The process contains a record of the dominant community (highlighted in red) following each merger operation, with the process continuing iteratively until all nodes in the graph converge into a single unified community.
• Figure 3: Demonstration of additional swaps caused by a lack of sufficient auxiliary qubits.
• Figure 4: Demonstration of Community Expansion when $k=1$ (painted in orange).
• Figure 5: Worst(chain-like) and optimal-case(pendent) scenarios, the red region represents the algorithm-determined qubits and edges, the blue region represents the neighboring qubits and adjacent edges expanded through community expansion at k=1, and the gray region represents other areas in the coupling graph.