RNN-VQE: a machine learning approach to generating variational ansatze

The variational quantum eigensolver (VQE) is a leading near-term hybrid classical/quantum algorithm for calculating spectra of molecular Hamiltonians. As with any variational approach, its performance depends sensitively on the selection of an appropriate variational form. Recent work has detailed the effectiveness of ADAPT-VQE, an adaptive approach to VQE in which the variational form is grown iteratively, resulting in ansatze which yield high performance with minimal numbers of variational parameters. This approach, however, is quantum resource intensive, requiring many quantum circuit executions and state measurements to grow the ansatze. Here we present RNN-VQE, a machine learning model which uses recurrent neural networks to learn and quickly generate effective variational ansatze for VQE.