Quantum neural networks force fields generation
arXiv:2203.04666 · doi:10.1088/2632-2153/ac7d3c
Abstract
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, quantum algorithms may notably be used to extend the reach of electronic structure calculations. On the other hand, quantum machine learning is also emerging as an alternative and promising path to quantum advantage. Here we follow this second route and establish a direct connection between classical and quantum solutions for learning neural network potentials. To this end, we design a quantum neural network architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum machine learning.
arXiv comment12 pages, 7 figures
References in corpus (40)
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- Machine learning and the physical sciences
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- Quantum computational chemistry
- Quantum Circuit Learning
- Machine Learning Force Fields
- Reconciling modern machine learning practice and the bias-variance trade-off
- Quantum Convolutional Neural Networks
- Parameterized quantum circuits as machine learning models
- Evaluating analytic gradients on quantum hardware
- Circuit-centric quantum classifiers
- The power of quantum neural networks
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Power of data in quantum machine learning
- Efficient Learning for Deep Quantum Neural Networks
- Data re-uploading for a universal quantum classifier
- How van der Waals interactions determine the unique properties of water
- Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
- An initialization strategy for addressing barren plateaus in parametrized quantum circuits
- Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
- WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Evidence for supercritical behavior of high-pressure liquid hydrogen
- Simulating a perceptron on a quantum computer
- Quantum computers as universal quantum simulators: state-of-art and perspectives
- Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
- Methodology for replacing indirect measurements with direct measurements
- Quantum computing models for artificial neural networks
- Unitary quantum perceptron as efficient universal approximator
- Quantum implementation of an artificial feed-forward neural network
- High-Dimensional Potential Energy Surfaces for Molecular Simulations
- Variational learning for quantum artificial neural networks
- Hybrid quantum-classical neural network for calculating ground state energies of molecules
- Microcanonical and finite temperature ab initio molecular dynamics simulations on quantum computers
- Quantum computing model of an artificial neuron with continuously valued input data
- Predicting excited states from ground state wavefunction by supervised quantum machine learning