Entanglement-assisted Hamiltonian dynamics learning

TL;DR

This paper introduces an entanglement-assisted approach to improve quantum Hamiltonian dynamics learning, addressing training challenges in quantum generative adversarial networks by coupling an auxiliary qubit to enhance performance.

Contribution

The novel entanglement-assisted strategy couples an auxiliary qubit during training, significantly improving quantum dynamics learning performance over existing methods.

Findings

Enhanced learning performance with entanglement-assisted method

Overcomes training plateaus and local minima issues

Effective for complex many-body Hamiltonian approximation

Abstract

Approximating the dynamics given by a complex many-body Hamiltonian with a simpler effective model lies at the interface of quantum Hamiltonian learning and quantum simulation. In this context, quantum generative adversarial networks (QGANs) have been shown to outperform standard Trotter-based approximations. However, their performance is often hindered by training plateaus and local minima that become increasingly severe with system size. To overcome these limitations, we propose an entanglement-assisted learning strategy that couples a single randomly initialized auxiliary qubit to the learning system at an intermediate stage of the training process. The interplay between randomization and entanglement significantly enhances the learning performance of the protocol.