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Validation of the State-of-the-Art Improved Quantum Approximate Optimization Algorithm

Quemix is committed to the research and development of quantum algorithms for solving optimization problems, and we are pleased to announce the publication of a detailed analysis of the effectiveness of state-of-the-art quantum approximate optimization algorithm.

"Systematic study on the dependence of the warm-start quantum approximate optimization algorithm on approximate solutions," Ken N. Okada, Hirofumi Nishi, Taichi Kosugi, and Yu-ichiro Matsushita, arXiv:2209.02942 (2022).

 Recently, the Quantum Approximate Optimization Algorithm (QAOA) has attracted much attention as an algorithm for solving optimization problems for small- and medium-scale quantum computers namely Noisy Intermediate-Scale Quantum (NISQ) devices. QAOA (Quantum Approximate Optimizaiton Algorithm) has been attracting attention. In this study, we focused on the state-of-the-art warm-start QAOA, which is an improved version of QAOA, and examined its effectiveness in detail. Warm-start QAOA is characterized by its ability to perform QAOA starting from approximate solutions obtained by classical computers or by ordinary QAOA. In this research, by using the approximate solution obtained from QAOA as the initial state, we have demonstrated through quantum simulations that the quantum circuit is shallower than QAOA and shows better characteristics than QAOA.

This warm-start QAOA can be run on NISQ and is expected to accelerate the application of small- and medium-scale quantum computers to specific optimization problems in society.


Quemix is committed to the research and development of quantum algorithms for solving optimization problems, and we are pleased to announce the publication of a detailed analysis of the effectiveness of state-of-the-art quantum approxim ate optimization algorithm.



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