The impact of optimization approximation algorithms on the performance of the BHT-QAOA Ali Al-Bayaty* [1] , Marek Perkowski [1]

https://www.academia.edu/3064-979X/2/4/10.20935/AcadQuant7996 This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean problems in various logical structures were examined and evaluated using the BHT-QAOA, these five optimization approximation algorithms, and a simulated noisy model of an IBM quantum computer. Broadly, the BHT-QAOA, with these five algorithms, successfully finds all optimized approximated solutions for these problems. Specifically, both the BFGS and SLSQP algorithms successfully find all best-approximated solutions for these problems, in the context of fewer function evaluations and higher quality of qubit measurements.