3 citations · 4 across the 12 of their papers we have counts for
15 papers
Symmetry without a manifold: intrinsic dimension on orbits
Chon-Fai Kam, Miloud Bessafi, Frédéric Cadet
The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input. On modular addition in that derivation h…
Structured Features Overfit Where Random Features Grok
Chon-Fai Kam, Miloud Bessafi, Frederic Cadet
Xu, Vardi and Safran (ICML 2026) prove that over-parameterized ridge regression over an unstructured random Gaussian feature map groks, with the delay between memorization and gene…
From Block-encoding to Generalized Quantum Signal Processing: Principles, Algorithms and Applications
Tal Gurfinkel, Kaushika De Silva, Anuradha Mahasinghe +9
Modern quantum algorithms are increasingly formulated as coherent procedures for implementing polynomial transformations of operators and singular values. This perspective provides…
Numerical Evaluation of ZX Calculus Optimization for Solovay Kitaev Quantum Circuit Synthesis
Dulari De Silva, Anuradha Mahasinghe, Chon-Fai Kam +3
Fault-tolerant architectures implement non-Clifford T gates through magic-state distillation, so the T-count of a synthesized circuit dominates its physical cost. The Solovay-Kitae…
An Exactness Barrier for ZX-Calculus Optimization of Synthesized Clifford+T Circuits
Chon-Fai Kam, Anuradha Mahasinghe, Kaushika De Silva +2
Gate synthesis and circuit optimization are usually studied separately, and evidence on their interaction is contradictory: ZX-calculus rewriting removes a stable fraction of Solov…
Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1
Neural networks trained on modular arithmetic exhibit grokking, a delayed transition from memorisation to generalisation known to depend on model capacity: too little and the netwo…
Quaternionic Response Geometry for Proteins: Toward a Noncommutative Theory of Ordered Deformations
Xiaoting Chen, Chon-Fai Kam, Yu Li +6
Protein function may depend not only on endpoint conformations but also on the ordered deformation histories through which they are reached. This distinction is relevant to alloste…
Assessing Cost Hamiltonian Reliability in Quantum Protein Structure Prediction
Mathieu Roget, Cedric Damour, Frederic Cadet +1
In variational quantum algorithms, QAOA, and quantum annealing, the cost Hamiltonian defines the optimization landscape explored by the quantum hardware; however, in many applicati…
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…
Hermitian Matrix Function Synthesis without Block-Encoding
Anuradha Mahasinghe, Kaushika De Silva, Xavier Cadet +3
Implementing polynomial functions of Hermitian matrices on quantum hardware is a foundational task in quantum computing, critical for accurate Hamiltonian simulation, quantum linea…
WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
Wenran Li, Xavier Cadet, Miloud Bessafi +7
The 3D incompressible Navier-Stokes equations model essential fluid phenomena, including turbulence and aerodynamics, but are challenging to solve due to nonlinearity and limited s…
Quantum Autoencoder: An efficient approach to quantum feature map generation
Shengxin Zhuang, Yusen Wu, Xavier F. Cadet +6
Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of…
EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Muhammad Daud, Philippe Charton, Cedric Damour +2
Understanding the relationship between protein sequences and their functions is fundamental to protein engineering, but this task is hindered by the combinatorially vast sequence s…
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
Wen-ran Li, Xavier F. Cadet, David Medina-Ortiz +6
Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great succes…
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine
Shengxin Zhuang, John Tanner, Yusen Wu +7
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a…