Circuitos quânticos variacionais simétricos e otimização bayesiana para determinação da energia de estado fundamental no trióxido de molibdénio

Abstract

This work investigates the application of the Variational Quantum Eigensolver (VQE) algorithm for the Molybdenum Trioxide (MoO3) molecule, focusing on the effective integration of different optimization strategies in quantum simulations. The study proposes the use of a reference state, defined by a genetic algorithm and combined in a symmetric variational circuit optimized for hardware. Additionally, Bayesian optimization is used to determine the ground state energy of MoO3. To demonstrate the validity of the constructed model, different benchmarks were carried out, comparing various circuit models and optimization algorithms. The process was divided into three stages: simulation without noise, simulation with noise, and execution in a real environment. In the noiseless simulation, different circuits and optimizers were compared with the approach proposed in this work. Then, in the noisy simulation, the two best optimizers from the previous stage were used for a new comparison. Finally, execution on a real IBM quantum computer, limited to 10 minutes, was performed. The overall results demonstrated the superiority of the proposed strategy, standing out for its convergence time, simplicity of the constructed circuit, and efficiency, even under limited conditions, suggesting that with more time, the accuracy could be even higher. This study contributes to the advancement of quantum computing applied to quantum chemistry, showing that the use of symmetric variational circuits combined with Bayesian optimization is the right path for VQE.