Efficient and practical Hamiltonian simulation from time-dependent product formulas

TL;DR

This paper introduces improved product formula-based quantum algorithms for simulating time evolution, offering better practical performance and scalability for systems with disparate energy scales, demonstrated through numerical simulations.

Contribution

The authors develop quantum algorithms with provably better scaling than naive Trotter formulas, optimized for systems with different energy scales, and show their practical efficiency via simulations.

Findings

Achieve an order of magnitude improvement in simulation size and time over standard Trotter formulas.

Algorithms are highly competitive in practice despite suboptimal theoretical scaling.

Numerical results demonstrate effectiveness in the 1D transverse-field Ising model.

Abstract

In this work we propose an approach for implementing time-evolution of a quantum system using product formulas. The quantum algorithms we develop have provably better scaling (in terms of gate complexity and circuit depth) than a naive application of well-known Trotter formulas, for systems where the evolution is determined by a Hamiltonian with different energy scales (i.e., one part is "large" and another part is "small"). Our algorithms generate a decomposition of the evolution operator into a product of simple unitaries that are directly implementable on a quantum computer. Although the theoretical scaling is suboptimal compared with state-of-the-art algorithms (e.g., quantum signal processing), the performance of the algorithms we propose is highly competitive in practice. We illustrate this via extensive numerical simulations for several models. For instance, in the strong-field regime of the 1D transverse-field Ising model, our algorithms achieve an improvement of one order of magnitude in both the system size and evolution time that can be simulated with a fixed budget of 1000 arbitrary 2-qubit gates, compared with standard Trotter formulas.