QMCkl: A Kernel Library for Quantum Monte Carlo Applications
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Slootman, Emiel Chilkuri, Vijay Gopal Delval, Aurelien Hoffer, Max Gorni, Tommaso Coppens, François van de Nes, Joris Panadés-Barrueta, Ramón L. Posenitskiy, Evgeny Ammar, Abdallah Borda, Edgar Josué Landinez Camus, Kevin Kohulàk, Oto Giner, Emmanuel Castro, Pablo de Oliveira Valensi, Cedric Jalby, William Filippi, Claudia Scemama, Anthony |
| author_facet | Slootman, Emiel Chilkuri, Vijay Gopal Delval, Aurelien Hoffer, Max Gorni, Tommaso Coppens, François van de Nes, Joris Panadés-Barrueta, Ramón L. Posenitskiy, Evgeny Ammar, Abdallah Borda, Edgar Josué Landinez Camus, Kevin Kohulàk, Oto Giner, Emmanuel Castro, Pablo de Oliveira Valensi, Cedric Jalby, William Filippi, Claudia Scemama, Anthony |
| contents | 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 API, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QMCkl: A Kernel Library for Quantum Monte Carlo Applications Slootman, Emiel Chilkuri, Vijay Gopal Delval, Aurelien Hoffer, Max Gorni, Tommaso Coppens, François van de Nes, Joris Panadés-Barrueta, Ramón L. Posenitskiy, Evgeny Ammar, Abdallah Borda, Edgar Josué Landinez Camus, Kevin Kohulàk, Oto Giner, Emmanuel Castro, Pablo de Oliveira Valensi, Cedric Jalby, William Filippi, Claudia Scemama, Anthony Chemical Physics Computational Physics 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 API, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, 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. |
| title | QMCkl: A Kernel Library for Quantum Monte Carlo Applications |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2512.16677 |