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Quantum Generative Model with Variable-Depth Circuit

Yiming Huang1, *, Hang Lei1, Xiaoyu Li1, *, Qingsheng Zhu2, Wanghao Ren3, Xusheng Liu2, 4

1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
2 School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China.
3 School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.
4 Department of Chemistry and Biochemistry, Utah State University, Logan, 84322, USA.

* Corresponding Author: Xiaoyu Li. Email: email.

Computers, Materials & Continua 2020, 65(1), 445-458. https://doi.org/10.32604/cmc.2020.010390

Abstract

In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this study, we address the inflexibility problem by adopting the variable-depth VQC model to automatically change the structure of the quantum circuit according to the qBAS score. The basic idea behind the variable-depth VQC is to consider the depth of the quantum circuit as a parameter during the training. Meanwhile, we compared the performance of the variable-depth VQC model based on four widely used statistical distances set as the loss functions, including Kullback-Leibler divergence (KL-divergence), Jensen-Shannon divergence (JS-divergence), total variation distance, and maximum mean discrepancy. Our numerical experiment shows a promising result that the variable-depth VQC model works better than the original VQC in the generative learning tasks.

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APA Style
Huang, Y., Lei, H., Li, X., Zhu, Q., Ren, W. et al. (2020). Quantum generative model with variable-depth circuit. Computers, Materials & Continua, 65(1), 445-458. https://doi.org/10.32604/cmc.2020.010390
Vancouver Style
Huang Y, Lei H, Li X, Zhu Q, Ren W, Liu X. Quantum generative model with variable-depth circuit. Comput Mater Contin. 2020;65(1):445-458 https://doi.org/10.32604/cmc.2020.010390
IEEE Style
Y. Huang, H. Lei, X. Li, Q. Zhu, W. Ren, and X. Liu "Quantum Generative Model with Variable-Depth Circuit," Comput. Mater. Contin., vol. 65, no. 1, pp. 445-458. 2020. https://doi.org/10.32604/cmc.2020.010390



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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