Mutual information-assisted Adaptive Variational Quantum Eigensolver Ansatz Construction

Abstract

Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers (VQE) on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem. Ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool in those algorithms. In this work, we propose a way to construct entangler pools with reduced size for those algorithms by leveraging classical algorithms. Our method uses mutual information (MI) between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group (DMRG) is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.