Spectral Analysis of Product Formulas for Quantum Simulation

TL;DR

This paper improves the understanding of Hamiltonian simulation using product formulas by analyzing spectral properties and perturbative effects, leading to better scaling of Trotter step size for quantum phase estimation and adiabatic simulation.

Contribution

It provides new spectral analysis techniques that improve Trotter step size scaling for quantum simulation methods under specific energy state assumptions.

Findings

Trotter step size for QPE scales as ε^{1/2} instead of ε.

Trotter error in DAS scales as M^{-2} instead of M^{-1}.

Results extend to diabatic processes with narrow energy bands.

Abstract

We consider Hamiltonian simulation using the first order Lie-Trotter product formula under the assumption that the initial state has a high overlap with an energy eigenstate, or a collection of eigenstates in a narrow energy band. This assumption is motivated by quantum phase estimation (QPE) and digital adiabatic simulation (DAS). Treating the effective Hamiltonian that generates the Trotterized time evolution using rigorous perturbative methods, we show that the Trotter step size needed to estimate an energy eigenvalue within precision $\epsilon$ using QPE can be improved in scaling from $\epsilon$ to $\epsilon^{1/2}$ for a large class of systems (including any Hamiltonian which can be decomposed as a sum of local terms or commuting layers that each have real-valued matrix elements). For DAS we improve the asymptotic scaling of the Trotter error with the total number of gates $M$ from $\mathcal{O}(M^{-1})$ to $\mathcal{O}(M^{-2})$, and for any fixed circuit depth we calculate an approximately optimal step size that balances the error contributions from Trotterization and the adiabatic approximation. These results partially generalize to diabatic processes, which remain in a narrow energy band separated from the rest of the spectrum by a gap, thereby contributing to the explanation of the observed similarities between the quantum approximate optimization algorithm and diabatic quantum annealing at small system sizes. Our analysis depends on the perturbation of eigenvectors as well as eigenvalues, and on quantifying the error using state fidelity (instead of the matrix norm of the difference of unitaries which is sensitive to an overall global phase).