Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation

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.