233 citations · 488 across the 11 of their papers we have counts for
13 papers
MOLPIPx: an end-to-end differentiable package for permutationally invariant polynomials in Python and Rust
Manuel S. Drehwald, Asma Jamali, Rodrigo A. Vargas-Hernández
In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate Permutationally Invariant Polynomials (PIPs) with modern machine learning frameworks, enablin…
Quantum Deep Equilibrium Models
Philipp Schleich, Marta Skreta, Lasse B. Kristensen +2
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth o…
GFlowNets for Hamiltonian decomposition in groups of compatible operators
Isaac L. Huidobro-Meezs, Jun Dai, Guillaume Rabusseau +1
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical…
Orbital-Free Density Functional Theory with Continuous Normalizing Flows
Alexandre de Camargo, Ricky T. Q. Chen, Rodrigo A. Vargas-Hernández
Orbital-free density functional theory (OF-DFT) provides an alternative approach for calculating the molecular electronic energy, relying solely on the electron density. In OF-DFT,…
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
R. A. Vargas-Hernández, K. Jorner, R. Pollice +1
Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model stil…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Differentiable quantum computational chemistry with PennyLane
Juan Miguel Arrazola, Soran Jahangiri, Alain Delgado +15
This work describes the theoretical foundation for all quantum chemistry functionality in PennyLane, a quantum computing software library specializing in quantum differentiable pro…
Gaussian Processes with Spectral Delta kernel for higher accurate Potential Energy surfaces for large molecules
Rodrigo A. Vargas-Hernández, Jake R. Gardner
The interpolation of high-dimensional potential energy surfaces (PESs) is commonly done with physically-inspired deep-neural network models. In this work, we illustrate that Gaussi…
Inverse design of dissipative quantum steady-states with implicit differentiation
Rodrigo A. Vargas-Hernández, Ricky T. Q. Chen, Kenneth A. Jung +1
Inverse design of a property that depends on the steady-state of an open quantum system is commonly done by grid-search type of methods. In this paper we present a new methodology…
Bayesian optimization for tuning and selecting hybrid-density functionals
Rodrigo A. Vargas-Hernández
The accuracy of some density functional (DF) models, widely used in material science, depends on empirical or free parameters which are commonly tuned using reference physical prop…
Physical extrapolation of quantum observables by generalization with Gaussian Processes
Rodrigo A. Vargas-Hernández, Roman V. Krems
For applications in chemistry and physics, machine learning (ML) is generally used to solve one of three problems: interpolation, classification or clustering. These problems use i…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…
Quantum walks assisted by particle number fluctuations
R. A. Vargas-Hernández, R. V. Krems
We study the spreading of a quantum particle placed in a single site of a lattice or binary tree with the Hamiltonian permitting particle number changes. We show that the particle…