  The quantum approximate optimization algorithm (QAOA) is one of the canonical
algorithms designed to find approximate solutions to combinatorial optimization
problems in current noisy intermediate-scale quantum (NISQ) devices. It is an
active area of research to exhibit its speedup over classical algorithms. The
performance of the QAOA at low depths is limited, while the QAOA at higher
depths is constrained by the current techniques. We propose a new version of
QAOA that incorporates the capabilities of QAOA and the real-space
renormalization group transformation, resulting in enhanced performance.
Numerical simulations demonstrate that our algorithm can provide accurate
solutions for certain randomly generated instances utilizing QAOA at low
depths, even at the lowest depth. The algorithm is suitable for NISQ devices to
exhibit a quantum advantage.
