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Trapped Ion Quantum Computing Quantum Simulation

Entanglement-assisted Hamiltonian dynamics learning

arXiv

Authors: Ayaka Usui, Guillermo Abad-López, Hari krishnan SV, Anna Sanpera, Some Sankar Bhattacharya

Year

2026

Paper ID

5806

Status

Preprint

Abstract Read

~2 min

Abstract Words

104

Citations

N/A

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.

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This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
It adds a 2026 reference point for readers tracking recent quantum research.
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.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2602.15931 [2] arXiv https://arxiv.org/abs/2602.15931 [3] Publisher https://arxiv.org/abs/2602.15931

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