Interaction-Aware Embedding Optimization for Neutral-Atom Quantum Processors
Abstract
Neutral atom-based quantum computers have the ability to dynamically reshape the qubit register geometry between experimental runs. This capability enables different types of problems to be mapped directly onto the hardware, significantly enhancing the efficiency of solving, for instance, complex optimization and machine learning use-cases. However, this flexibility requires finding atom positions to implement target pairwise interactions, inducing a difficult nonconvex optimization for setting up the quantum machine. In this work, we introduce a new force-directed method that targets interactions to encode the input problem on the quantum computer. Numerical results show that our new method can improve embedding feasibility and solution quality by orders of magnitude, and remains reliable on the largest instances.
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BibTeX
@misc{montleau2026interactionaware,
title = {{Interaction-Aware Embedding Optimization for Neutral-Atom Quantum Processors}},
author = {Clément de Terrasson de Montleau and Victor Drouin-Touchette and Wesley Coelho},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.04068},
eprint = {2610.04068},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.04068},
}
RIS
TY - UNPB
TI - Interaction-Aware Embedding Optimization for Neutral-Atom Quantum Processors
AU - Clément de Terrasson de Montleau
AU - Victor Drouin-Touchette
AU - Wesley Coelho
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.04068
UR - https://arxiv.org/abs/2610.04068
AB - Neutral atom-based quantum computers have the ability to dynamically reshape the qubit register geometry between experimental runs. This capability enables different types of problems to be mapped directly onto the hardware, significantly enhancing the efficiency of solving, for instance, complex optimization and machine learning use-cases. However, this flexibility requires finding atom positions to implement target pairwise interactions, inducing a difficult nonconvex optimization for setting up the quantum machine. In this work, we introduce a new force-directed method that targets interactions to encode the input problem on the quantum computer. Numerical results show that our new method can improve embedding feasibility and solution quality by orders of magnitude, and remains reliable on the largest instances.
ER -