Quantum Extreme Learning of molecular potential energy surfaces and force fields

Abstract

Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.

14 pages, 7 figures. Accepted on Machine Learning: Science and Technology

References in corpus (24)

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Quantum Circuit Learning
• Evaluating analytic gradients on quantum hardware
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Harnessing disordered quantum dynamics for machine learning
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Quantum machine learning beyond kernel methods
• Evidence for supercritical behavior of high-pressure liquid hydrogen
• Quantum Machine Learning for Chemistry and Physics
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• Any Quantum Network is Structurally Controllable by a Single Driving Signal
• Dynamical phase transitions in quantum reservoir computing
• Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
• Temporal Information Processing on Noisy Quantum Computers
• Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
• Bayesian machine learning for quantum molecular dynamics
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Potential and limitations of quantum extreme learning machines
• Machine-learning-corrected quantum dynamics calculations
• Shadow tomography on general measurement frames
• Quantum Gaussian process model of potential energy surface for a polyatomic molecule
• Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules

Cited by in corpus (6)

• Symmetry-invariant quantum machine learning force fields
• Quantum simulations of complex systems
• Predicting fermionic densities using a Projected Quantum Kernel method
• Towards Improved Quantum Machine Learning for Molecular Force Fields
• Benchmarking of quantum fidelity kernels for Gaussian process regression
• Practical Quantum Reservoir Computing in Rydberg Atom Arrays