  We present a hybrid quantum classical neural network that can be trained to
perform electronic structure calculation and generate potential energy curves
of simple molecules. The method is based on the combination of parameterized
quantum circuits and measurements. With unsupervised training, the neural
network can generate electronic potential energy curves based on training at
certain bond lengths. To demonstrate the power of the proposed new method, we
present results of using the quantum-classical hybrid neural network to
calculate ground state potential energy curves of simple molecules such as
H$_2$, LiH and BeH$_2$. The results are very accurate and the approach could
potentially be used to generate complex molecular potential energy surfaces.
