Quantum neural networks force fields generation

@article{Kiss2022QuantumNN,
  title={Quantum neural networks force fields generation},
  author={Oriel Kiss and Francesco Tacchino and Sofia Vallecorsa and Ivano Tavernelli},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.04666}
}
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers have recently started to offer new viable computational paradigms to tackle such problems. On the one hand… 

Figures and Tables from this paper

Conditional Born machine for Monte Carlo events generation
Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as random source. So called Born machines are purely quantum models

References

SHOWING 1-10 OF 65 REFERENCES
Provably efficient machine learning for quantum many-body problems
TLDR
It is proved that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonian in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.
Molecular Dynamics with Neural Network Potentials
TLDR
The present text discusses several practical aspects of conducting machine learning driven molecular dynamics simulations and analysis of a machine learning based model for simulating molecular dipole moments in the framework of predicting infrared spectra via Molecular dynamics simulations show that machine learning models can offer valuable aid in understanding chemical systems beyond a simple prediction of quantities.
Quantum machine learning beyond kernel methods
TLDR
This work identifies the first unifying framework that captures all standard models based on parametrized quantum circuits: that oflinear quantum models, and shows how data re-uploading circuits, a generalization of linear models, can be efficiently mapped into equivalent linear quantum models.
Quantum Neuron: an elementary building block for machine learning on quantum computers
TLDR
A small quantum circuit is proposed that naturally simulates neurons with threshold activation and defines a building block, the "quantum neuron", that can reproduce a variety of classical neural network constructions while maintaining the ability to process superpositions of inputs and preserve quantum coherence and entanglement.
Quantum advantage in learning from experiments
TLDR
This research presents a probabilistic model of the black hole that combines quantum entanglement, superposition, and superposition to describe the fabric of space-time.
Theory of overparametrization in quantum neural networks
TLDR
This paper presents a meta-analyses of the LaSalle–Cerezo–Larocca–Bouchut–Seiden–Stein cellular automaton, a model derived from the model developed by J. J. Giambiagi in 2007, which states that the model derived in this paper can be modified for flows on rugous topographies varying around an inclined plane.
Deep double descent: where bigger models and more data hurt
TLDR
The notion of model complexity allows us to identify certain regimes where increasing the number of train samples actually hurts test performance, and defines a new complexity measure called the effective model complexity and conjecture a generalized double descent with respect to this measure.
Effective dimension of machine learning models
TLDR
It is proved that the local effective dimension bounds the generalization error and the aptness of this capacity measure for machine learning models is discussed.
d.
TLDR
The novel JEV RT-NASBA assay could be used as an efficient molecular biology tool to diagnose JEV, which would facilitate the surveillance of reproductive failure disease in swine and would be beneficial for public health security.
NAT
Apesar da gestão de carreiras ser uma preocupação recente no quadro da gestão das relações de trabalho, é hoje evidente a crise em que se encontra a forma tradicional de abordagem desta problemática.
...
...