Growth reduction of similarity transformed electronic Hamiltonians in qubit space

TL;DR

This paper introduces an efficient sampling method for similarity transformations of electronic Hamiltonians in quantum computing, reducing the growth of terms and improving energy estimates in molecular simulations.

Contribution

It proposes a generator selection technique that minimizes new Hamiltonian terms while lowering energy, enhancing VQE efficiency for molecular systems.

Findings

The method reduces the number of terms in the transformed Hamiltonian.

It achieves systematic energy lowering in molecular models.

The approach outperforms other selection strategies in benchmarks.

Abstract

Accurately solving the electronic structure problem through the variational quantum eigensolver (VQE) is hindered by the available quantum resources of current and near-term devices. One approach to relieving the circuit depth requirements for VQE is to "pre-process" the electronic Hamiltonian by a similarity transformation incorporating some degree of electronic correlation, with the remaining correlation left to be addressed by the circuit ansatz. This often comes at the price of a substantial increase in the number of terms to measure in the similarity transformed Hamiltonian. In this work, we propose an efficient approach to sampling elements from the complete Pauli group for $N$ qubits which minimize the onset of new terms in the transformed Hamiltonian, while facilitating substantial energy lowering. We benchmark the growth-mitigating generator selection technique for ground state energy estimations applied to models of the H$_4$, N$_2$ and H$_2$O molecular systems. It is found that utilizing a selection procedure which obtains the growth-minimizing generator from the set of operators with maximal energy gradient is the most competitive approach to reducing the onset of Hamiltonian terms while achieving systematic energy lowering of the reference state.