Formal dequantizability analysis of QEMFN

Establish a formal analysis of the dequantizability of Quantum Entangled Multimodal Fusion Networks through tensor-network methods or complexity-theoretic arguments.

Background

Quantum Entangled Multimodal Fusion Networks (QEMFN) are evaluated at a scale that remains classically simulable and are compared with a dequantized paired-topology classical baseline. Although QEMFN shows a modest empirical performance improvement over that baseline, the paper explicitly cautions that the comparison does not exclude stronger classical surrogates, including tensor-network-inspired or random-feature methods.

The unresolved problem is to determine formally whether the QEMFN construction can be efficiently dequantized, particularly by analyzing the tensor-network bond dimension or by using complexity-theoretic arguments. Such an analysis would clarify whether the observed behavior reflects a genuinely nonclassical computational resource or an efficiently simulable structured classical mechanism.

References

The present comparisons do not rule out stronger tensor-network-inspired, random-feature, or further-optimized compact classical surrogates, and the reported gain should therefore be interpreted only within the evaluated baseline set. A formal analysis of dequantizability through tensor-network methods or complexity-theoretic arguments is left for future work .

— Quantum Entangled Multimodal Fusion Networks (QEMFN): Resource-Aware Hybrid Vision-Language Fusion via Trainable Entanglement  (2610.08216 - Alla et al., 6 Oct 2026) in Discussion section