Practical quantum approximate optimization algorithms on IBM quantum processors

Greg Medwig; Sua Choi; Paulo C. Castillo; Kwangmin Yu

doi:10.1117/12.3002940

13 March 2024 Practical quantum approximate optimization algorithms on IBM quantum processors

Greg Medwig, Sua Choi, Paulo C. Castillo, Kwangmin Yu

Proceedings Volume 12911, Quantum Computing, Communication, and Simulation IV; 129111G (2024) https://doi.org/10.1117/12.3002940
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

In recent years, significant progress has been made in building quantum computers by several companies. 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 of the length of the QAOA ansatz on IBM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.

Citation Download Citation

Greg Medwig, Sua Choi, Paulo C. Castillo, and Kwangmin Yu "Practical quantum approximate optimization algorithms on IBM quantum processors", Proc. SPIE 12911, Quantum Computing, Communication, and Simulation IV, 129111G (13 March 2024); https://doi.org/10.1117/12.3002940

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