  Quantum machine learning has established as an interdisciplinary field to
overcome limitations of classical machine learning and neural networks. This is
a field of research which can prove that quantum computers are able to solve
problems with complex correlations between inputs that can be hard for
classical computers. This suggests that learning models made on quantum
computers may be more powerful for applications, potentially faster computation
and better generalization on less data. The objective of this paper is to
investigate how training of quantum neural network (QNNs) can be done using
quantum optimization algorithms for improving the performance and time
complexity of QNNs. A classical neural network can be partially quantized to
create a hybrid quantum-classical neural network which is used mainly in
classification and image recognition. In this paper, a QNN structure is made
where a variational parameterized circuit is incorporated as an input layer
named as Variational Quantum Neural Network (VQNNs). We encode the cost
function of QNNs onto relative phases of a superposition state in the Hilbert
space of the network parameters. The parameters are tuned with an iterative
quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs
is experimented with MNIST digit recognition (less complex) and crack image
classification datasets (more complex) which converges the computation in
lesser time than QNN with decent training accuracy.
