We are pleased to announce the publication of our paper "Genetic-Multi-initial Generalized VQE: Advanced VQE method using Genetic Algorithms then Local Search".
I am pleased to announce that we have published a paper with Dr. Tomono. The title is "Genetic-Multi-initial Generalized VQE: Advanced VQE method using Genetic Algorithms then Local Search". This is a summary of the computational results of Generalized-Multi-Initial Generalized VQE (GMIG-VQE) presented at IOP Quantum 2020. This paper compares the results of four different classical optimization methods for GMIG-VQE: Powell, BFGS, Nelder-Mead, and Newton methods on molecular hydrogen. GMIG-VQE uses Genetic Algorithm then Local Search (GA then LS) as a classical optimization method. GMIG-VQE uses Genetic Algorithm then Local Search (GA then LS) as the classical optimization method, which is a real-valued genetic algorithm that searches for global minima of parameters as individuals, and finally multiplies some of the individuals with the lowest energy by the classical optimization method (LS) to find the optimal solution. As a result, it was confirmed that GMIG-VQE, which uses the Newton method as an optimization method, can calculate the excited states with the highest accuracy. This method is expected to be further accelerated and improved in accuracy by speeding up the genetic algorithm part.