3 citations · 4 across the 7 of their papers we have counts for
8 papers
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…
Retrodictive Forecasting: A Proof-of-Concept for Exploiting Temporal Asymmetry in Time Series Prediction
Cedric Damour
We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via i…
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…