Variational quantum Hamiltonian engineering

TL;DR

This paper introduces a variational quantum algorithm called VQHE that reduces the Pauli norm of Hamiltonians, thereby decreasing the quantum computational overhead for expectation value estimation and simulation tasks.

Contribution

It develops a theory to encode Pauli norm minimization as an L1-norm problem and demonstrates its effectiveness through numerical experiments on Ising and molecular Hamiltonians.

Findings

VQHE effectively reduces the Pauli norm of Hamiltonians.

Numerical results show decreased overhead in quantum expectation estimation.

The method is applicable to various Hamiltonian types.

Abstract

The Hamiltonian of a quantum system is represented in terms of operators corresponding to the kinetic and potential energies of the system. The expectation value of a Hamiltonian and Hamiltonian simulation are two of the most fundamental tasks in quantum computation. The overheads for realizing the two tasks are determined by the Pauli norm of Hamiltonian, which sums over all the absolute values of Pauli coefficients. In this work, we propose a variational quantum algorithm (VQA) called variational quantum Hamiltonian engineering (VQHE) to minimize the Pauli norm of Hamiltonian, such that the overhead for executing expectation value estimation and Hamiltonian simulation can be reduced. First, we develop a theory to encode the Pauli norm optimization problem into the vector L1-norm minimization problem. Then we devise an appropriate cost function and utilize the parameterized quantum circuits (PQC) to minimize the cost function. We also conduct numerical experiments to reduce the Pauli norm of the Ising Hamiltonian and molecules' Hamiltonian to show the efficiency of the proposed VQHE.