Symmetry-invariant quantum machine learning force fields

arXiv (Cornell University)

Abstract

Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data. However, the trainability and scalability of such models are still limited, due to both theoretical and practical barriers. Inspired by recent developments in geometric classical and quantum machine learning, here we design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.

Authors 5

1. Isabel Nha Minh Le Aachen Institute for Quantum Information

   IBM Research - Zurich · RWTH Aachen University

   Affiliation as printed

   IBM Quantum, IBM Research Europe -Zurich, 8803 Rueschlikon, Switzerland

   Institute for Quantum Information, RWTH Aachen University, 52074 Aachen, Germany

2. Oriel Kiss

   University of Geneva · European Organization for Nuclear Research

   Affiliation as printed

   Department of Nuclear and Particle Physics , University of Geneva , 1211 Geneva , Switzerland

   European Organization for Nuclear Research (CERN) , 1211 Geneva , Switzerland

   Department of Nuclear and Particle Physics, University of Geneva, 1211 Geneva, Switzerland

   European Organization for Nuclear Research (CERN), 1211 Geneva, Switzerland

3. Julian Schuhmacher

   IBM Research - Zurich

   Affiliation as printed

   IBM Quantum , IBM Research Europe -Zurich , 8803 Rueschlikon , Switzerland

   IBM Quantum, IBM Research Europe -Zurich, 8803 Rueschlikon, Switzerland

4. Ivano Tavernelli

   IBM Research - Zurich

   Affiliation as printed

   IBM Quantum , IBM Research Europe -Zurich , 8803 Rueschlikon , Switzerland

   IBM Quantum, IBM Research Europe -Zurich, 8803 Rueschlikon, Switzerland

5. Francesco Tacchino

   IBM Research - Zurich

   Affiliation as printed

   IBM Quantum , IBM Research Europe -Zurich , 8803 Rueschlikon , Switzerland

   IBM Quantum, IBM Research Europe -Zurich, 8803 Rueschlikon, Switzerland