An Efficient Iterative Algorithm for Qubit Mapping Via Layer‐Weight Assignment and Search Space Reduction

Abstract Current quantum devices support interactions only between physically adjacent qubits, preventing quantum circuits from being directly executed on these devices. Therefore, SWAP gates are required to remap logical qubits to physical qubits, which in turn increases both quantum resource consumption and error rates. To minimize the insertion of additional SWAP gates, HAIL is proposed, an efficient iterative qubit mapping algorithm. Leveraging the inherent parallelism in quantum circuits, a new layer‐weight assignment method is integrated with subgraph isomorphism to derive an optimal initial qubit mapping. Moreover, a two‐stage SWAP sequence search algorithm is presented that effectively identifies the most efficient SWAP sequence by distilling feasible SWAP sequences at different stages. The whole qubit mapping algorithm is then refined through a few iterative bidirectional traversals, further reducing the number of SWAP gates required. Experimental results on the IBM Q20 architecture and various benchmarks show that HAIL‐3 reduces the number of additional gates inserted in the by 20.62% compared to state‐of‐the‐art algorithms. Moreover, a partially extended SWAP sequence strategy is proposed in combination with HAIL to reduce its time complexity, with experiments on the sparsely connected Google Sycamore architecture demonstrating reductions in both algorithm runtime and additional SWAP gates.

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Publication Details

Journal
Advanced Quantum Technologies
Published
2025-06-11
DOI
https://doi.org/10.1002/qute.202500125
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

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article

An Efficient Iterative Algorithm for Qubit Mapping Via Layer‐Weight Assignment and Search Space Reduction

Kang Xu, Yukun Wang, Dandan Li, Zeyang Li et al.
Advanced Quantum Technologies
Quantum Computing Algorithms and Architecture
article

An Efficient Iterative Algorithm for Qubit Mapping Via Layer‐Weight Assignment and Search Space Reduction

Kang Xu, Yukun Wang, Dandan Li, Zeyang Li, Xinjian Liu
article en

Abstract

Abstract Current quantum devices support interactions only between physically adjacent qubits, preventing quantum circuits from being directly executed on these devices. Therefore, SWAP gates are required to remap logical qubits to physical qubits, which in turn increases both quantum resource consumption and error rates. To minimize the insertion of additional SWAP gates, HAIL is proposed, an efficient iterative qubit mapping algorithm. Leveraging the inherent parallelism in quantum circuits, a new layer‐weight assignment method is integrated with subgraph isomorphism to derive an optimal initial qubit mapping. Moreover, a two‐stage SWAP sequence search algorithm is presented that effectively identifies the most efficient SWAP sequence by distilling feasible SWAP sequences at different stages. The whole qubit mapping algorithm is then refined through a few iterative bidirectional traversals, further reducing the number of SWAP gates required. Experimental results on the IBM Q20 architecture and various benchmarks show that HAIL‐3 reduces the number of additional gates inserted in the by 20.62% compared to state‐of‐the‐art algorithms. Moreover, a partially extended SWAP sequence strategy is proposed in combination with HAIL to reduce its time complexity, with experiments on the sparsely connected Google Sycamore architecture demonstrating reductions in both algorithm runtime and additional SWAP gates.

Advanced Quantum TechnologiesVol. 8(10)
Beijing University of Posts and Telecommunications (CN), China University of Petroleum, Beijing (CN), Institute of Computing Technology (CN)
National Natural Science Foundation of China, China University of Petroleum, Beijing
Openalex Percentile: Top 93%
Quantum Computing Algorithms and Architecture
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An Efficient Iterative Algorithm for Qubit Mapping Via Layer‐Weight Assignment and Search Space Reduction — Kang Xu, Yukun Wang, et al. · Advanced Quantum Technologies (2025) | TGRS Research Map | TGRS