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The paper introduces NS-RIS, a scalable Newton-Schulz retraction-based algorithm for learning hidden quantum Markov models on the Stiefel manifold, providing the first mathematical performance guarantee and empirical evidence that HQMMs can outperform EM-trained HMMs on non-quantum-generated data.
This paper proposes a lightweight framework using sticky factorial HDP-HMMs to model conversational emotion as latent regimes from multimodal valence-arousal trajectories, aiming for interpretable and computationally efficient emotional state tracking.