State preparation and evolution in quantum computing: a perspective from Hamiltonian moments

TL;DR

This paper reviews quantum algorithms based on Hamiltonian moments for NISQ devices, demonstrating their implementation on IBM-Q hardware to estimate energies and magnetization in quantum systems, and discusses future prospects.

Contribution

It provides a comprehensive review and practical tutorial on computing Hamiltonian moments using NISQ hardware, including a case study on the Heisenberg model.

Findings

Successful measurement of Hamiltonian moments on IBM-Q hardware

Accurate estimation of energy and magnetization in a four-site Heisenberg model

Discussion of practical issues and future applications of these algorithms

Abstract

Quantum algorithms on the noisy intermediate-scale quantum (NISQ) devices are expected to simulate quantum systems that are classically intractable to demonstrate quantum advantages. However, the non-negligible gate error on the NISQ devices impedes the conventional quantum algorithms to be implemented. Practical strategies usually exploit hybrid quantum classical algorithms to demonstrate potentially useful applications of quantum computing in the NISQ era. Among the numerous hybrid algorithms, recent efforts highlight the development of quantum algorithms based upon quantum computed Hamiltonian moments, $\langle \phi | \hat{\mathcal{H}}^n | \phi \rangle$ ($n=1,2,\cdots$), with respect to quantum state $|\phi\rangle$. In this tutorial, we will give a brief review of these quantum algorithms with focuses on the typical ways of computing Hamiltonian moments using quantum hardware and improving the accuracy of the estimated state energies based on the quantum computed moments. Furthermore, we will present a tutorial to show how we can measure and compute the Hamiltonian moments of a four-site Heisenberg model, and compute the energy and magnetization of the model utilizing the imaginary time evolution in the real IBM-Q NISQ hardware environment. Along this line, we will further discuss some practical issues associated with these algorithms. We will conclude this tutorial review by overviewing some possible developments and applications in this direction in the near future.