Open Access
ARTICLE
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: .
Computers, Materials & Continua 2020, 65(1), 445-458. https://doi.org/10.32604/cmc.2020.010390
Received 01 March 2020; Accepted 20 May 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
Y. Huang, H. Lei, X. Li, Q. Zhu, W. Ren
et al., "Quantum generative model with variable-depth circuit,"
Computers, Materials & Continua, vol. 65, no.1, pp. 445–458, 2020. https://doi.org/10.32604/cmc.2020.010390