Multi-product Hamiltonian simulation with explicit commutator scaling

TL;DR

This paper provides a rigorous analysis of the multi-product formula for Hamiltonian simulation, demonstrating explicit commutator scaling and near-optimal complexity, leading to significant speedups over existing methods.

Contribution

It offers a rigorous complexity analysis of the well-conditioned MPF, establishing explicit commutator scaling and near-optimal dependence on time and precision.

Findings

Achieves polynomial speedup in system size and evolution time.

Attains exponential speedup in precision.

Outperforms second-order and higher-order product formulas.

Abstract

The well-conditioned multi-product formula (MPF), proposed by [Low, Kliuchnikov, and Wiebe, 2019], is a simple high-order time-independent Hamiltonian simulation algorithm that implements a linear combination of standard product formulas of low order. While the MPF aims to simultaneously exploit commutator scaling among Hamiltonians and achieve near-optimal time and precision dependence, its lack of a rigorous error bound on the nested commutators renders its practical advantage ambiguous. In this work, we conduct a rigorous complexity analysis of the well-conditioned MPF, demonstrating explicit commutator scaling and near-optimal time and precision dependence at the same time. Using our improved complexity analysis, we present several applications of practical interest where the MPF based on a second-order product formula can achieve a polynomial speedup in both system size and evolution time, as well as an exponential speedup in precision, compared to second-order and even higher-order product formulas. Compared to post-Trotter methods, the MPF based on a second-order product formula can achieve polynomially better scaling in system size, with only poly-logarithmic overhead in evolution time and precision.