Yiming Huang1, *, Hang Lei1, Xiaoyu Li1, *, Qingsheng Zhu2, Wanghao Ren3, Xusheng Liu2, 4
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI: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… More >
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