  Variational quantum algorithms, which consist of optimal parameterized
quantum circuits, are promising for demonstrating quantum advantages in the
noisy intermediate-scale quantum (NISQ) era. Apart from classical computational
resources, different kinds of quantum resources have their contributions to the
process of computing, such as information scrambling and entanglement.
Characterizing the relation between the complexity of specific problems and
quantum resources consumed by solving these problems is helpful for us to
understand the structure of VQAs in the context of quantum information
processing. In this work, we focus on the quantum approximate optimization
algorithm (QAOA), which aims to solve combinatorial optimization problems. We
study information scrambling and entanglement in QAOA circuits, respectively,
and discover that for a harder problem, more quantum resource is required for
the QAOA circuit to obtain the solution in most cases. We note that in the
future, our results can be used to benchmark the complexity of quantum
many-body problems by information scrambling or entanglement accumulation in
the computing process.
