Hybrid quantum-classical algorithms for solving quantum chemistry in Hamiltonian-wave-function space

The variational quantum eigensolver (VQE) typically optimizes variational parameters in a quantum circuit to prepare eigenstates for a quantum system. Its application to many problems may involve a group of Hamiltonians, e.g., a Hamiltonian of a molecule is a function of the nuclear configurations. In this paper we incorporate derivatives of the Hamiltonian into the VQE and develop some hybrid quantum-classical algorithms, which explore both Hamiltonian and wave-function spaces for solving quantum chemistry problems more efficiently. For geometry optimization, we propose a mutual gradient-descent algorithm that updates the parameters of the Hamiltonian and wave function alternately, which can give a rapid convergence towards equilibrium structures of molecules as demonstrated in numerical simulations. Moreover, to speed up the calculation of the energy potential surface, we establish differential equations that govern how the optimized variational parameters of the wave function change with the intrinsic parameters of the Hamiltonian. Our study suggests a direction for a hybrid quantum-classical algorithm for solving quantum systems more efficiently by considering spaces of both the Hamiltonian and the wave function.

Automated summary

In this paper, a hybrid quantum-classical algorithm is proposed to optimize variational parameters in a quantum circuit by incorporating derivatives of the Hamiltonian for more efficient solutions to quantum chemistry problems. Additionally, a mutual gradient-descent algorithm is introduced for geometry optimization to quickly converge towards equilibrium structures of molecules. By considering spaces of both the Hamiltonian and the wave function, our study suggests a direction for a hybrid quantum-classical algorithm to solve quantum systems more efficiently.