  Training quantum neural networks (QNNs) using gradient-based or gradient-free
classical optimisation approaches is severely impacted by the presence of
barren plateaus in the cost landscapes. In this paper, we devise a framework
for leveraging quantum optimisation algorithms to find optimal parameters of
QNNs for certain tasks. To achieve this, we coherently 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
optimisation structure using adaptively selected Hamiltonians. The quantum
mechanism of this framework exploits hidden structure in the QNN optimisation
problem and hence is expected to provide beyond-Grover speed up, mitigating the
barren plateau issue.
