When do quantum views help? Complementary classical-quantum representations in multi-view fusion
Read the original on arXiv AI →The paper investigates when quantum machine learning can enhance predictive performance by fusing classical and quantum representations in multi‑view learning. It introduces a framework that preserves full classical inputs while adding simulated quantum features, and uses concatenation and attention to combine the views. Experiments on synthetic angular‑polynomial data and real datasets (Wine, Wisconsin Diagnostic Breast Cancer, Fashion‑MNIST, Steel Plates Faults) show that fusion improves predictions for targets that depend on complementary classical and quantum relationships, while classical‑only baselines perform best on other tasks.
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