An approximate diagonalization method for large scale Hamiltonians

TL;DR

This paper introduces an approximate diagonalization technique combining exact diagonalization and perturbation theory to efficiently compute low energy states of large Hamiltonians, demonstrated on systems with up to 128 qubits.

Contribution

The paper presents a novel hybrid method that reduces computational complexity by deriving effective Hamiltonians for faster diagonalization of large-scale quantum systems.

Findings

Accurately computes low energy eigenvalues for large Hamiltonians

Significantly reduces diagonalization time compared to exact methods

Validated on systems with up to 128 qubits

Abstract

An approximate diagonalization method is proposed that combines exact diagonalization and perturbation expansion to calculate low energy eigenvalues and eigenfunctions of a Hamiltonian. The method involves deriving an effective Hamiltonian for each eigenvalue to be calculated, using perturbation expansion, and extracting the eigenvalue from the diagonalization of the effective Hamiltonian. The size of the effective Hamiltonian can be significantly smaller than that of the original Hamiltonian, hence the diagonalization can be done much faster. We compare the results of our method with those obtained using exact diagonalization and quantum Monte Carlo calculation for random problem instances with up to 128 qubits.