  Quantum Machine Learning is an emerging sub-field in machine learning where
one of the goals is to perform pattern recognition tasks by encoding data into
quantum states. This extension from classical to quantum domain has been made
possible due to the development of hybrid quantum-classical algorithms that
allow a parameterized quantum circuit to be optimized using gradient based
algorithms that run on a classical computer. The similarities in training of
these hybrid algorithms and classical neural networks has further led to the
development of Quantum Neural Networks (QNNs). However, in the current training
regime for QNNs, the gradients w.r.t objective function have to be computed on
the quantum device. This computation is highly non-scalable and is affected by
hardware and sampling noise present in the current generation of quantum
hardware. In this paper, we propose a training algorithm that does not rely on
gradient information. Specifically, we introduce a novel meta-optimization
algorithm that trains a \emph{meta-optimizer} network to output parameters for
the quantum circuit such that the objective function is minimized. We
empirically and theoretically show that we achieve a better quality minima in
fewer circuit evaluations than existing gradient based algorithms on different
datasets.
