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This paper investigates leveraging architectural diversity in variational quantum circuits (VQCs) and classical models for improved predictive performance by applying Combinatorial Fusion Analysis (CFA). By selecting and combining architecturally diverse models based on rank-score functions and cognitive diversity, the research demonstrates that hybrid classical-quantum ensembles significantly outperform individual models across four binary classification benchmarks. The CFA-based fusion consistently achieved accuracy improvements of up to 6.03%, surpassing existing literature benchmarks.
Architecturally diverse quantum-classical models fused via Combinatorial Fusion Analysis consistently outperform individual models and literature baselines, unlocking significant accuracy gains on classification tasks.
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.
