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Symmetry-invariant quantum machine learning force fields
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.
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems.
Trainability barriers and opportunities in quantum generative modeling
In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration.
Conditional Born machine for Monte Carlo event generation
So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers.
Quantum neural networks force fields generation
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales.
