A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping

Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.

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Publication Details

Journal
IEEE Transactions on Computers
Published
2024-08-12
DOI
https://doi.org/10.1109/tc.2024.3441825
Citations
9
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
3.13

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A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping

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IEEE Transactions on Computers
Quantum Computing Algorithms and Architecture
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article

A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping

Bing Guo, Shengxin Dai, Yan Shen, Kejun Bi, Kui Ye, Yuchuan Hu, Mingjie Zhao, Fei Chen, Chuanjie Liu
article en
9 citations

Abstract

Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.

IEEE Transactions on ComputersVol. 73(12)
Sichuan University (CN), Chengdu University of Information Technology (CN), Chengdu University (CN), Changhong (China) (CN)
National Natural Science Foundation of China, Sichuan Province Science and Technology Support Program, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 7%
Quantum Computing Algorithms and Architecture
3.13
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