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A progressive predictor-based quantum architecture search with active learning

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Abstract

Quantum architecture search (QAS) algorithms have demonstrated remarkable proficiency in automatically designing high-performance quantum circuits for variational quantum algorithms. Predictor-based QAS (PQAS) is an effective technique to accelerate QAS. It estimates the approximate performances of quantum circuits using a neural network trained on a small subset of circuits and their corresponding ground-truth performances. However, current PQAS algorithms train a single predictor to fit the entire circuit space using limited training samples, which is inefficient and unnecessary, as QAS aims to identify the optimal quantum circuit. In this paper, we propose a progressive PQAS with active learning (PQAS-AL), which gradually trains more precise predictors for subspaces where high-performance circuits reside. PQAS-AL follows an iterative process from coarse to fine. During the early iterations, coarse predictors are trained to identify relatively good subspaces. As the algorithm iterates, an increasing number of high-performance circuits are added to the training set, resulting in the continuous enhancement of the predictor’s ability to recognize high-performance circuits. Moreover, the iterative training of multiple predictors in PQAS-AL also enables the utilization of newly acquired quantum circuits and their corresponding ground-truth performances, resulting in significantly improved sample efficiency. Unlike current QAS algorithms, PQAS-AL naturally integrates the search and performance evaluation modules. Simulation on the Variational Quantum Eigensolver (VQE) for the Heisenberg model demonstrates that PQAS-AL achieves a 2.8\(\times \) reduction in labeled quantum circuits for the same performance compared to the current PQAS.

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Data Availability Statement

This manuscript has associated data in a data repository [Authors’ comment: All data included in this manuscript are available upon request by contacting with the corresponding author.]

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Acknowledgements

This work is supported by Guangdong Basic and Applied Basic Research Foundation (Nos. 2022A1515140116, 2022A15 15010101, 2021A1515011985), National Natural Science Foundation of China (No.  61972091), and Innovation Program for Quantum Science and Technology (No. 2021ZD0302900). SZ was supported by the Major Key Project of PCL.

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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

    Maijie Deng

  3. Peng Cheng Laboratory, Shenzhen, 518055, China

    Shenggen Zheng

  4. College of Computer and Information Engineering, Henan Normal University, **nxiang, 453007, China

    Fei Zhang

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

    Haozhen Situ

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  1. Maijie Deng
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Correspondence to Zhimin He.

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Deng, M., He, Z., Zheng, S. et al. A progressive predictor-based quantum architecture search with active learning. Eur. Phys. J. Plus 138, 905 (2023). https://doi.org/10.1140/epjp/s13360-023-04537-6

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