Practical quantum approximate optimization algorithms on IBM quantum processors

Abstract

In recent years, significant progress has been made in building quantum computers by several c ompanies. Despite the progress, these noisy intermediate-scale quantum (NISQ) computers still suffer from several noises and errors such as measurement errors, multi-qubit gate errors, and worse, short decoherence times. Though quantum computer vendors are releasing better quantum computers in terms of Quantum Volume, the quantum device still remains far from quantum supremacy in practical problems. The Quantum Approximate Optimization Algorithm (QAOA) was suggested to potentially demonstrate a computational advantage in combinatorial optimization problems on NISQ computers. In this paper, we optimize the QAOA circuits and to scale the problem size on IBM quantum processors. In addition, we study the effect o f t he l ength o f t he Q AOA a nsatz o n I BM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.