Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion

Abstract:Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural diversity across quantum circuit search spaces can be used to produce stronger predictions through model fusion. We apply Combinatorial Fusion Analysis (CFA), a model fusion framework that selects and combines multiple scoring systems using rank-score function (RSF) and cognitive diversity (CD), to a pool of 29 architecturally diverse classical models and quantum circuits. Using a diversity-aware model selection method, we identify subsets of maximally diverse models and combine them with both score and rank combination. Experiments on four binary classification benchmarks covering different domains demonstrate that CFA-based hybrid classical-quantum fusion consistently outperform the best individual model on every dataset considered with improvements up to 6.03% in accuracy. When comparing with relevant literatures, the CFA framework further achieves superior performance across all four datasets.

Quantum Physics (quant-ph); Machine Learning (cs.LG)

arXiv:2610.13808 [quant-ph]

(or arXiv:2610.13808v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.13808 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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