  Variational quantum algorithms (VQAs) are widely speculated to deliver
quantum advantages for practical problems under the quantum-classical hybrid
computational paradigm in the near term. Both theoretical and practical
developments of VQAs share many similarities with those of deep learning. For
instance, a key component of VQAs is the design of task-dependent parameterized
quantum circuits (PQCs) as in the case of designing a good neural architecture
in deep learning. Partly inspired by the recent success of AutoML and neural
architecture search (NAS), quantum architecture search (QAS) is a collection of
methods devised to engineer an optimal task-specific PQC. It has been proven
that QAS-designed VQAs can outperform expert-crafted VQAs under various
scenarios. In this work, we propose to use a neural network based predictor as
the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can
discover powerful PQCs, yielding state-of-the-art results for various examples
from quantum simulation and quantum machine learning. Notably, neural predictor
guided QAS provides a better solution than that by the random-search baseline
while using an order of magnitude less of circuit evaluations. Moreover, the
predictor for QAS as well as the optimal ansatz found by QAS can both be
transferred and generalized to address similar problems.
