92 citations · 276 across the 18 of their papers we have counts for
18 papers
The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
Narjes Ansari, César Feniou, Nicolaï Gouraud +13
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio mo…
High Performance Quantum Emulation for Chemistry Applications with Hyperion
Olivier Adjoua, Siwar Badreddine, César Feniou +4
The strategic demand for quantum hardware currently outpaces the availability of near-term devices, necessitating high-performance software emulators to validate novel protocols. W…
Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
Nicolaï Gouraud, Côme Cattin, Thomas Plé +3
Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC)…
An Optimized Construction of Lie Algebra Generator Pools for Variational Quantum Eigensolvers in Chemistry
Yaromir Viswanathan, Olivier Adjoua, César Feniou +2
Lie algebras are essential mathematical structures used in physics to describe sets of quantum operators. Identifying a minimal set of generators to construct these algebras is a c…
Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
Côme Cattin, Thomas Plé, Olivier Adjoua +3
We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network wh…
Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals
Anouar Benali, Thomas Plé, Olivier Adjoua +18
We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learnin…
Velocity Jumps for Molecular Dynamics
Nicolaï Gouraud, Louis Lagardère, Olivier Adjoua +3
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical La…
Non-Iterative Disentangled Unitary Coupled-Cluster based on Lie-algebraic structure
Mohammad Haidar, Olivier Adjoua, Siwar Baddredine +2
Due to their non-iterative nature, fixed Unitary Coupled-Cluster (UCC) ansätze are attractive for performing quantum chemistry Variational Quantum Eigensolver (VQE) computations as…
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
Thomas Plé, Olivier Adjoua, Louis Lagardère +1
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-i…
Shortcut to Chemically Accurate Quantum Computing via Density-based Basis-set Correction
Diata Traore, Olivier Adjoua, César Feniou +8
Using GPU-accelerated state-vector emulation, we propose to embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC) to obtai…
Sparse Quantum State Preparation for Strongly Correlated Systems
C. Feniou, O. Adjoua, B. Claudon +3
Quantum Computing allows, in principle, the encoding of the exponentially scaling many-electron wave function onto a linearly scaling qubit register, offering a promising solution…
Lambda-ABF: Simplified, Portable, Accurate and Cost-effective Alchemical Free Energy Computations
Louis Lagardère, Lise Maurin, Olivier Adjoua +4
We introduce an efficient and robust method to compute alchemical free energy differences, resulting from the application of multiple walker Adaptive Biasing Force (ABF) in conjunc…
Greedy Gradient-free Adaptive Variational Quantum Algorithms on a Noisy Intermediate Scale Quantum Computer
César Feniou, Muhammad Hassan, Baptiste Claudon +4
Hybrid quantum-classical adaptive Variational Quantum Eigensolvers (VQE) hold the potential to outperform classical computing for simulating many-body quantum systems. However, pra…
Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials
Thomas Plé, Nastasia Mauger, Olivier Adjoua +4
We report the implementation of a multi-CPU and multi-GPU massively parallel platform dedicated to the explicit inclusion of nuclear quantum effects (NQEs) in the Tinker-HP molecul…
Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua +5
Deep-HP is a scalable extension of the \TinkerHP\ multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP incr…
Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory
Pier Paolo Poier, Théo Jaffrelot Inizan, Olivier Adjoua +2
Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD…
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems
Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly +9
We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamic…
Reduced-order modeling of hemodynamics across macroscopic through mesoscopic circulation scales
Olivier Adjoua, Stéphanie Pitre-Champagnat, Didier Lucor
We propose a hemodynamic reduced-order model bridging macroscopic and meso-scopic blood flow circulation scales from arteries to capillaries. In silico tree like vascular geometrie…