25 citations · 50 across the 7 of their papers we have counts for
8 papers
E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU
Olivier Peltre, Armand Picard, Adrien Pichard +6
We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU. Leveraging both optimized CUDA and Pallas kernels and…
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
Christoph Brunken, Titouan Cormier, Lucien Walewski +15
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is…
Integral Formulas for Vector Signal Tensor Products
Valentin Heyraud, Zachary Weller-Davies, Jules Tilly
We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor product to anti-symmetric couplings…
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet +11
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of…
Unified Framework for Matchgate Classical Shadows
Valentin Heyraud, Héloise Chomet, Jules Tilly
Estimating quantum fermionic properties is a computationally difficult yet crucial task for the study of electronic systems. Recent developments have begun to address this challeng…
Efficient estimation of trainability for variational quantum circuits
Valentin Heyraud, Zejian Li, Kaelan Donatella +2
Parameterized quantum circuits used as variational ansätze are emerging as promising tools to tackle complex problems ranging from quantum chemistry to combinatorial optimization.…
Machine learning via relativity-inspired quantum dynamics
Zejian Li, Valentin Heyraud, Kaelan Donatella +2
We present a machine-learning scheme based on the relativistic dynamics of a quantum system, namely a quantum detector inside a cavity resonator. An equivalent analog model can be…
Noisy Quantum Kernel Machines
Valentin Heyraud, Zejian Li, Zakari Denis +2
In the noisy intermediate-scale quantum era, an important goal is the conception of implementable algorithms that exploit the rich dynamics of quantum systems and the high dimensio…