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From the 2 of 13 linked papers with an AI index.

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13 papers

q-bio.BM2026

Quaternionic Response Geometry for Proteins: Toward a Noncommutative Theory of Ordered Deformations

Xiaoting Chen, Chon-Fai Kam, Yu Li +6

Protein function may depend on both endpoint conformations and the ordered deformation histories by which they are reached. This distinction is relevant to allostery, conformationa…

cs.LG2026

Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations

Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1

The paper analyzes two‑layer neural networks with holomorphic monomial activations (σ(z)=z^k) on modular arithmetic tasks, providing an exact algebraic condition for when a target…

quant-ph2026

Near-Optimal Mode Scaling for Finite-Dimensional Boson Sampling via Lie-Algebraic Leakage Bounds

Chon-Fai Kam, En-Jui Kuo

The paper develops a Lie‑algebraic framework to bound leakage in finite‑dimensional boson sampling and shows that the required number of modes scales near‑optimally with the number…

quant-ph2026

Majorana Constellations: A Geometric Lens on Multipartite Entanglement and Geometric Phases

Chon-Fai Kam

The Majorana stellar representation maps a pure spin- state to points on a sphere. This review develops it with entanglement as the organising principle, and two objects re…

quant-ph2026

Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability

Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1

While world models learn compact representations of complex environments, they lack a physics-grounded metric to assess the structural fidelity of their latent spaces. We identify…

quant-ph2026

Non-variational supervised quantum kernel methods: a review

John Tanner, Chon-Fai Kam, Jingbo Wang

Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning. Unlike variational quantum algorithms, which rely on gradient-based opt…