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Grover search inspired alternating operator ansatz of quantum approximate optimization algorithm for search problems
Chen-Fu Chiang (SUNY Polytechnic Institute), Paul M. Alsing (United States Air Force Research Laboratory, U.S. Air Force Research Laboratory Information Directorate)
Abstract: Abstract 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.