  The current era of quantum computing has yielded several algorithms that
promise high computational efficiency. While the algorithms are sound in theory
and can provide potentially exponential speedup, there is little guidance on
how to design proper quantum circuits to realize the appropriate unitary
transformation to be applied to the input quantum state. In this paper, we
present \texttt{QArchSearch}, an AI based quantum architecture search package
with the \texttt{QTensor} library as a backend that provides a principled and
automated approach to finding the best model given a task and input quantum
state. We show that the search package is able to efficiently scale the search
to large quantum circuits and enables the exploration of more complex models
for different quantum applications. \texttt{QArchSearch} runs at scale and high
efficiency on high-performance computing systems using a two-level
parallelization scheme on both CPUs and GPUs, which has been demonstrated on
the Polaris supercomputer.
