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A GNN-based predictor for quantum architecture search

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Abstract

The performance of the variational quantum algorithm (VQA) highly depends on the structure of the quantum circuit. Quantum architecture search (QAS) algorithm aims to automatically search out high-performance quantum circuits for given VQA tasks. However, current QAS algorithms need to calculate the ground-truth performances of a large number of quantum circuits during the searching process, especially for large-scale quantum circuits, which is very time-consuming. In this paper, we propose a predictor based on a graph neural network (GNN), which can largely reduce the computational complexity of the performance evaluation and accelerate the QAS algorithm. We denote the quantum circuit with a directed acyclic graph (DAG), which can well represent the structural and topological information of the quantum circuit. A GNN-based encoder with an asynchronous message-passing scheme is used to encode discrete circuit structures into continuous feature representations, which mimics the computational routine of a quantum circuit on the quantum data. Simulations on the 6-qubit and 10-qubit variational quantum eigensolver (VQE) show that the proposed predictor can learn the latent relationship between circuit structures and their performances. It effectively filters out poorly performing circuits and samples the most promising quantum circuits for evaluation, which avoids a significant computational cost in the performance evaluation and largely improves the sample efficiency.

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Data Availability

No data were used during the study. All codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by Guangdong Basic and Applied Basic Research Foundation (Nos. 2021A1515012138, 2022A1515140116, 2021A1515012639), Key Platform, Research Project of Education Department of Guangdong Province (No. 2020KTSCX132), Key Research Project of Universities in Guangdong Province (No. 2019KZDXM007), National Natural Science Foundation of China (No.  61972091) and Student Academic Foundation of Foshan University (No. xsjj202202kjb13).

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Author notes

  1. Zhimin He and Xuefen Zhang authors have contributed equally to this work.

Authors and Affiliations

  1. School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China

    Zhimin He & Yan Zhou

  2. School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528000, China

    Xuefen Zhang

  3. Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China

    Chuangtao Chen

  4. School of Economics and Management, Wuyi University, Jiangmen, 529020, China

    Zhiming Huang

  5. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Haozhen Situ

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  1. Zhimin He
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Correspondence to Chuangtao Chen.

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Appendices

Appendix A: Pseudocode of DAG representation

figure a

Appendix B: Hyperparameters in the simulations

We implement the simulations on a classical computer with a CPU i9-10900K using PennyLane[44], which includes a wide range of quantum machine learning libraries. The predictor is trained with Adam optimizer [45]. The hyperparameters of the proposed predictor for 6-qubit and 10-qubit VQEs are shown in Table and Table , respectively.

Table 1 The hyperparameters of the proposed predictor for 6-qubit VQE
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Table 2 The hyperparameters of the proposed predictor for 10-qubit VQE
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He, Z., Zhang, X., Chen, C. et al. A GNN-based predictor for quantum architecture search. Quantum Inf Process 22, 128 (2023). https://doi.org/10.1007/s11128-023-03881-x

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