Efficient time-evolution of matrix product states using average Hamiltonians

TL;DR

This paper introduces an improved second-order method for simulating the dynamics of quantum many-body systems using matrix product states, significantly enhancing accuracy and convergence over traditional first-order methods.

Contribution

The authors develop a simple, efficient second-order augmentation to existing MPS algorithms for time-dependent Hamiltonians, improving simulation accuracy and convergence speed.

Findings

Achieved about 1000-fold error reduction for NV center chains

Demonstrated faster convergence with second-order method

Applied to realistic quantum systems with promising results

Abstract

Simulating quantum many-body systems (QMBS) is one of the long-standing, highly non-trivial challenges in condensed matter physics and quantum information due to the exponentially growing size of the system's Hilbert space. To date, tensor networks have been an essential tool for studying such quantum systems, owing to their ability to efficiently capture the entanglement properties of the systems they represent. One of the well-known tensor network architectures, namely matrix product states (MPS), is the standard method for simulating one-dimensional QMBS. Here, we propose a simple, yet efficient, method to augment the already available MPS algorithms to simulate the dynamics of time-dependent Hamiltonians with better accuracy and a faster convergence rate, giving a second-order convergence compared to the first-order convergence of the standard method. We apply our proposed method to simulate the dynamics of a chain of single spins associated with nitrogen-vacancy color centers in diamonds, which has potential applications for practical and scalable quantum technologies, and find that our method improves the average error for a system of few NV centers by a factor of about 1000 for moderate step sizes. Our work paves the way for efficient simulation of QMBS under the influence of time-dependent Hamiltonians.