Newton-Schulz retraction-based inference enables hidden quantum Markov models to outperform classical HMMs
Read the original at arxiv.org→arXiv:2608.06554v1 Announce Type: new Abstract: Hidden Markov models (HMMs) are widely used probabilistic models for discrete sequential data but can be limited when hidden dynamics are complex. Hidden quantum...
Original headline: "Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs"
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- Aug 10, 04:00 UTC arXiv cs.LG lead source Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs