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Quantum computing has the potential to outperform classical computers and isexpected to play an active role in various fields. In quantum machine learning,a quantum computer has been found useful for enhanced feature representationand high dimensional state or function approximation. Quantum-Classical hybridalgorithms are proposed in recent years for this purpose under theNoisy-Intermediate Scale Quantum computer (NISQ) environment. Under thisscheme, the role played by classical computer is the parameter tuning,parameter optimization, and parameter update for the quantum circuit. In thispaper, we propose a gradient descent based backpropagation algorithm that canefficiently calculate the gradient in parameter optimization and update theparameter for quantum circuit learning, which outperforms the current parametersearch algorithms in terms of computing speed while presents the same or evenhigher test accuracy.

Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation