  Quantum computing has the potential to outperform classical computers and is
expected to play an active role in various fields. In quantum machine learning,
a quantum computer has been found useful for enhanced feature representation
and high dimensional state or function approximation. Quantum-Classical hybrid
algorithms are proposed in recent years for this purpose under the
Noisy-Intermediate Scale Quantum computer (NISQ) environment. Under this
scheme, the role played by classical computer is the parameter tuning,
parameter optimization, and parameter update for the quantum circuit. In this
paper, we propose a gradient descent based backpropagation algorithm that can
efficiently calculate the gradient in parameter optimization and update the
parameter for quantum circuit learning, which outperforms the current parameter
search algorithms in terms of computing speed while presents the same or even
higher test accuracy.
