Quantum-Classical Mixture of Experts for Topological Advantage
Analysis
This paper explores a hybrid quantum-classical approach to the Mixture-of-Experts (MoE) architecture, aiming to overcome limitations in classical routing. The core idea is to use a quantum router, leveraging quantum feature maps and wave interference, to achieve superior parameter efficiency and handle complex, non-linear data separation. The research focuses on demonstrating a 'topological advantage' by effectively untangling data distributions that classical routers struggle with. The study includes an ablation study, noise robustness analysis, and discusses potential applications.
Key Takeaways
- •Proposes a Hybrid Quantum-Classical Mixture of Experts (QMoE) architecture.
- •Uses a Quantum Router based on quantum feature maps and wave interference.
- •Demonstrates a 'topological advantage' in separating non-linearly separable data.
- •Shows robustness against simulated quantum noise.
- •Suggests applications in federated learning and privacy-preserving machine learning.
“The 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.”