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.
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BibTeX
@misc{xu2026diversityaware,
title = {{Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion}},
author = {Jingyan Xu and Yuanhong Wu and Samuel Yen-Chi Chen and Sean Patten and Christina Schweikert and D. Frank Hsu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13808},
eprint = {2610.13808},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13808},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion
AU - Jingyan Xu
AU - Yuanhong Wu
AU - Samuel Yen-Chi Chen
AU - Sean Patten
AU - Christina Schweikert
AU - D. Frank Hsu
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13808
UR - https://arxiv.org/abs/2610.13808
AB - 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.
ER -