  We use the mapping between two computation frameworks , Adiabatic Grover
Search (AGS) and Adiabatic Quantum Computing (AQC), to translate the Grover
search algorithm into the AQC regime. We then apply Trotterization on the
schedule-dependent Hamiltonian of AGS to obtain the values of variational
parameters in the Quantum Approximate Optimization Algorithm (QAOA) framework.
The goal is to carry the optimal behavior of Grover search algorithm into the
QAOA framework without the iterative machine learning processes.
