QMCkl: A kernel library for quantum Monte Carlo applications

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Authors: E. Slootman, Vijay Gopal Chilkuri, Aurélien Delval, Max Hoffer, Tommaso Gorni, François Coppens, J. Nes, Ramón L. Panadés‐Barrueta, Evgeny Posenitskiy, Abdallah Ammar, Edgar Josué Landinez Borda, Kevin Camus
+7 more Oto Kohulák, Emmanuel Giner, Pablo de Oliveira Castro, Cédric Valensi, William Jalby, Claudia Filippi, Anthony Scemama
Cited by: 0
References: 28

Abstract

Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance kernels implementing the core building blocks of QMC calculations. It offers a C-compatible application programming interface, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, the Jastrow factor, and the necessary derivatives also to perform variational and structural optimization. QMCkl separates algorithmic development from hardware-specific tuning by combining human-readable reference implementations with performance-optimized kernels that produce identical numerical results. The library enables consistent, efficient, and reproducible simulations across different QMC codes and architectures and achieves substantial speedups in the evaluation of the energy and its derivatives. Beyond QMC, QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools, promoting cross-code interoperability and simplifying high-performance scientific software development.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Chemical Physics Studies Quantum superfluid helium dynamics

Publication details

Journal / source
The Journal of Chemical Physics
Volume
164
Issue
11
Publication date
18 March 2026
Language
EN