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Quantum Machine Learning

Evaluating Parameter Transfer in FALQON Across Graph Families

arXiv
Authors: Alisson dos Passos Fumaco, Marcos Vinicius Reballo, Fernando Augusto Caletti de Barros, Gabriel Fernandes Thomaz, Eduardo I. Duzzioni

Year

2026

Paper ID

67571

Status

Preprint

Abstract Read

~2 min

Abstract Words

94

Citations

0

Abstract

We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors $n in \{8,10,12\}$ to 14-node recipients. Using 3-regular and Erdős-Rényi families, we show that transfer success is dictated by the recipient graph, not the donor. Transfer excels for dense recipients - achieving high approximation ratios regardless of the donor - but remains challenging in sparse cross-family cases. Crucially, performance is highly resilient to donor size, with 8-node donors matching larger instances. Thus, cheap small graphs can provide robust parameters for larger targets, significantly reducing the measurement overhead of the feedback loop.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors n in 8,10,12 to 14-node recipients.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2605.29917 [2] arXiv https://arxiv.org/abs/2605.29917 [3] Publisher https://arxiv.org/abs/2605.29917

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal • updated 2026-06-14 02:30:21

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