aiqbench analysis
This research presents a novel force-directed optimization method specifically designed to map complex problems onto the dynamic geometry of neutral-atom quantum processors. By significantly improving embedding feasibility and solution quality, the approach directly addresses a critical bottleneck in leveraging hardware flexibility for advanced machine learning and optimization tasks. The work represents a substantive advancement in the practical utility of neutral-atom systems for real-world computational challenges.
Interaction-Aware Embedding Optimization for Neutral-Atom Quantum Processors
arXiv:2610.04068v1 Announce Type: new 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.
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.
Quantum Physics (quant-ph); Optimization and Control (math.OC)
arXiv:2610.04068 [quant-ph]
(or arXiv:2610.04068v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.04068 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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