Yi-Ming Huang1, Xiao-Yu Li1,*, Yi-Xuan Zhu1, Hang Lei1, Qing-Sheng Zhu2, Shan Yang3
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 789-803, 2021, DOI:10.32604/cmc.2021.016663
Abstract Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientific research and improving data processing. How to efficiently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing. It can be described as learning or approximating a unitary operator. Since the success of the hybrid-based quantum machine learning model proposed in recent years, we investigate to apply the techniques from QML to tackle this problem. Based on the Choi–Jamiołkowski isomorphism in quantum computing, we transfer the original problem of learning… More >
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