Quantum Circuit Learning Using Error Backpropagation

Masaya Watabe, Koudai Shiba, Katsuyoshi Sakamoto, Tomah Sogabe


Quantum computing has the potential to outperform classical computers, and is expected to play an active role in various fields. On quantum machine learning, it is difficult to learn only on quantum computing. Classical-quantum hybrid algorithms are proposed in recent years. Classical computer is used for calculation of parameter tuning in quantum circuit. In this paper, we propose a backpropagation algorithm that can efficiently calculate gradient in optimization of parameter in quantum circuit, which outperforms the current parameter search algorithm while presents the same or even higher accuracy.


quantum computing; machine learning; error backpropagation; gradient

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