Adaptive random quantum eigensolver

We propose an adaptive random quantum algorithm to obtain optimized eigensolver. Specifically, we introduce a general method parametrize and optimize the probability density function of number generator, which is core stochastic algorithms. follow bioinspired evolutionary mutation changes in involved matrices. Our optimization based on two figures merit: learning speed accuracy. This provides high fidelities for searched eigenvectors faster convergence way advantage with current noisy intermediate-scaled computers.