Solving strongly correlated electron models on a quantum computer

TL;DR

This paper demonstrates how to simulate the Hubbard model on a quantum computer, including ground state preparation, evolution, and measurement strategies, enabling detailed analysis of strongly correlated electron systems.

Contribution

It introduces efficient quantum algorithms and circuits for preparing ground states, evolving the Hubbard model, and measuring properties, advancing quantum simulation capabilities.

Findings

Gate complexity scales as O(N) per time step

Circuit depth scales as O(log N)

Measurement methods reduce errors quadratically

Abstract

One of the main applications of future quantum computers will be the simulation of quantum models. While the evolution of a quantum state under a Hamiltonian is straightforward (if sometimes expensive), using quantum computers to determine the ground state phase diagram of a quantum model and the properties of its phases is more involved. Using the Hubbard model as a prototypical example, we here show all the steps necessary to determine its phase diagram and ground state properties on a quantum computer. In particular, we discuss strategies for efficiently determining and preparing the ground state of the Hubbard model starting from various mean-field states with broken symmetry. We present an efficient procedure to prepare arbitrary Slater determinants as initial states and present the complete set of quantum circuits needed to evolve from these to the ground state of the Hubbard model. We show that, using efficient nesting of the various terms each time step in the evolution can be performed with just $\mathcal{O}(N)$ gates and $\mathcal{O}(\log N)$ circuit depth. We give explicit circuits to measure arbitrary local observables and static and dynamic correlation functions, both in the time and frequency domain. We further present efficient non-destructive approaches to measurement that avoid the need to re-prepare the ground state after each measurement and that quadratically reduce the measurement error.