Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning

arXiv:2609.28425 · quant-ph, cs.LG · Submitted 2026-09-23 · Read on arXiv

quant-ph, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Admissibility — 31× in this paper · explained in 2 episodes
• Calibration — 11× in this paper · explained in 7 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Trace — 5× in this paper · explained in 2 episodes
• Data Assimilation — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Principal Component Analysis (PCA) — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• All-to-All Connectivity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Block Encoding — 1× in this paper · explained in 4 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• QUBO Formulation — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
• Learning Neural PDE Solvers with Convergence Guarantees
• A quantum algorithm for the Kalman filter using block encoding
• Quantum Deep Learning: A Comprehensive Review

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