Quantum Information & Computation
Volume 26
RESEARCH ARTICLE Open access

Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network

Shouli He, Wen Liu, Yangzhi Li, Maoduo Li, Kai Chen, Yaohua Lu

Author affiliations
  • Shouli He1
  • Wen Liu2
  • Yangzhi Li3
  • Maoduo Li1
  • Kai Chen1
  • Yaohua Lu4
  1. School of Computer and Cyber Sciences, Communication University of China, Beijing, China
  2. School of Computer and Cyber Sciences, Communication University of China, Beijing, China; State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China; Laboratory of Convergent Media and Intelligent Technology (Communication University of China), Ministry of Education, Beijing, China
  3. School of Artificial Intelligence, Changsha University of Science and Technology, Changsha 410114, Hunan, China
  4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

pp. 38-67 · 10.2478/qic-2026-0003

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Abstract

Quantum circuit compilation is an indispensable part of executing quantum circuits on quantum devices. It translates logical quantum circuits into physical quantum circuits that meet the device constraints. However, nonspecialist users require extensive expertise to effectively select and evaluate quantum circuit compilation processes. Therefore, this paper proposes a quantum circuit compilation option prediction method based on Deep Residual Attention Neural Network. The method aims to automate the selection of the best compilation scheme and enhance the efficiency and accuracy of quantum circuit compilation. Firstly, a quantum circuit feature extraction algorithm based on Time-Weighted Interaction Graph is proposed. This algorithm can effectively represent quantum circuits using fixed-length vectors, making the model input scalable. Secondly, for existing one-dimensional features, a quantum circuit compilation option prediction model based on Deep Residual Attention Neural Network is designed and trained. This model is capable of quickly and accurately predicting the optimal compilation option combination. Extensive experimental evaluations and comparisons are carried out on the MQT (Munich Quantum Toolkit) quantum circuit compilation dataset. These evaluations verify the effectiveness and advancement of the proposed method. Compared with the current state-of-the-art method (Random Forest), the proposed method improves the accuracy by 5.44%, the Top-3 accuracy by 2.6%, and the F1 by 2.05%.
Open accessCC BY-NC-ND 4.0

Keywords

  • quantum circuit compilation
  • deep residual neural network
  • attention mechanism
  • quantum computing
  • deep Learning

Suggested citation

Shouli He, Wen Liu, Yangzhi Li, Maoduo Li, Kai Chen, Yaohua Lu (2026). Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network. Quantum Information & Computation, 26(1), 38-67. https://doi.org/10.2478/qic-2026-0003

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