  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.
