Given the current promise of quantum computing to increase the efficiency of optimization algorithms, it is important to utilize these quantum algorithms for useful applications. The quantum approximate optimization algorithm was recently developed to accommodate combinatorial optimization problems where the minimum of a cost function is required for an optimal solution. We consider appropriate cost Hamiltonians of the long-range spin glass Ising model with the objectives to solve for an unknown a priori ground state and an unknown sequence of unitary operators to move an initial Hadamard quantum state to an arbitrary eigenstate of the Hamiltonian. We utilize a path dependent quantum approximate optimization algorithm optimized by considering the pairwise correlation of angles on its path to the global optimum of the entire angle parameter space. An importance sampling technique is used to distribute the probability of selecting a specific pair of angles to optimize along its path as it evolves with the depth of the quantum circuit. We also simulate the measurement process classically to resemble the sample outputs of a quantum computer and to determine how the sampling affects the performance of this algorithm. Lastly, results are demonstrated to justify this new approach to the quantum approximate optimization algorithm as a viable technique to solve quantum computational optimization problems.