  Quantum approximate optimization algorithm (QAOA) is one of the popular
quantum algorithms that are used to solve combinatorial optimization problems
via approximations. QAOA is able to be evaluated on both physical and virtual
quantum computers simulated by classical computers, with virtual ones being
favored for their noise-free feature and availability. Nevertheless, performing
QAOA on virtual quantum computers suffers from a slow simulation speed for
solving combinatorial optimization problems which require large-scale quantum
circuit simulation (QCS). In this paper, we propose techniques to accelerate
QCS for QAOA using mathematical optimizations to compress quantum operations,
incorporating efficient bitwise operations to further lower the computational
complexity, and leveraging different levels of parallelisms from modern
multi-core processors, with a study case to show the effectiveness on solving
max-cut problems.
