Abstract
Variational Quantum Eigensolver (VQE) is a variational method to solve eigen problems by a classical optimization algorithm to adjust the parameters of the preparation of quantum states. It is simple but its accuracy is affected by local optimization and the expression limit of ansatz space. To solve this problem, the Imaginary Time Evolution (ITE) is used to lower the computed eigen energy which is monotonically decreasing with certainty. To accelerate the ITE algorithm, we are going to replace the ITE operator with Gaussian form operator and test the efficiency and accuracy. At last, we plan to combine VQE and generative query network for quantum state learning (GQNQ), using GQNQ to find the more accurate quantum state around the state produced by VQE. After the learning, the problems whether the parametric quantum state (a vector) can express the real state and how to test it need to be solved.
Anyone interested is welcome to attend.