Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings

arXiv:2602.18364 · cs.IT, cs.LG, math.IT, quant-ph, stat.ML · Submitted 2026-02-20 · Read on arXiv

cs.IT, cs.LG, math.IT, quant-ph, stat.ML

Submitted: 2026-02-20

Updated: 2026-09-02

Terminology

• Hilbert Space — 67× in this paper · explained in 2 episodes
• Embeddings — 37× in this paper · explained in 2 episodes
• Trace — 35× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Entanglement — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Quantum Circuits — 2× in this paper · explained in 2 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 3 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Trace Distance — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Fundamentals of quantum Boltzmann machine learning with visible and hidden units

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