Hybrid Quantum-Classical Mixture of Experts: Unlocking Topological Advantage via Interference-Based Routing

TL;DR

This paper introduces a hybrid quantum-classical Mixture of Experts architecture that leverages quantum interference to improve data routing, achieving better separation of complex data distributions and demonstrating robustness on near-term quantum hardware.

Contribution

It proposes a novel Quantum Router using wave interference as a high-dimensional kernel, providing a quantum advantage in modeling complex decision boundaries over classical methods.

Findings

Quantum Router outperforms classical routers on non-linear datasets

Wave interference enables modeling of complex, non-linear decision boundaries

Architecture remains robust under simulated quantum noise

Abstract

The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of classical routing mechanisms. This paper investigates the potential of Quantum Machine Learning (QML) to address these limitations through a novel Hybrid Quantum-Classical Mixture of Experts (QMoE) architecture. Specifically, we conduct an ablation study using a Quantum Gating Network (Router) combined with classical experts to isolate the source of quantum advantage. Our central finding validates the Interference Hypothesis: by leveraging quantum feature maps (Angle Embedding) and wave interference, the Quantum Router acts as a high-dimensional kernel method, enabling the modeling of complex, non-linear decision boundaries with superior parameter efficiency compared to its classical counterparts. Experimental results on non-linearly separable data, such as the Two Moons dataset, demonstrate that the Quantum Router achieves a significant topological advantage, effectively "untangling" data distributions that linear classical routers fail to separate efficiently. Furthermore, we analyze the architecture's robustness against simulated quantum noise, confirming its feasibility for near-term intermediate-scale quantum (NISQ) hardware. We discuss practical applications in federated learning, privacy-preserving machine learning, and adaptive systems that could benefit from this quantum-enhanced routing paradigm.