Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial 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.
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Key points
- 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.
- 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.
Sources (1)
- [1]Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial FusionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:20 PM
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.
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.
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