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Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

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Quantum Gaussian process model of potential energy surface for a polyatomic molecule.

J Dai¹, R V Krems¹

¹Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, CanadaStewart Blusson Quantum Matter Institute, Vancouver, British Columbia V6T 1Z4, Canada.

The Journal of Chemical Physics

|May 14, 2022

Summary

Quantum kernels from quantum computers accurately model molecular potential energy surfaces. Bayesian optimization refines these quantum Gaussian processes for precise regression, even extrapolating complex molecular energy landscapes.

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Area of Science:

Quantum computing
Computational chemistry
Machine learning

Background:

Reproducing kernel Hilbert spaces (RKHS) are crucial in machine learning.
Quantum computing offers novel methods for kernel generation.
Accurate potential energy surfaces (PESs) are vital for molecular simulations.

Purpose of the Study:

To develop accurate regression models for molecular potential energy surfaces (PESs) using quantum kernels.
To investigate the feasibility of using current quantum computers for this task.
To explore the impact of quantum kernel properties on model performance.

Main Methods:

Encoding multi-dimensional vectors into quantum computer gates.
Projecting quantum states onto qubit states to generate quantum kernels.
Employing Bayesian optimization to tune quantum gate parameters.
Building Gaussian process models with the derived quantum kernels.

Main Results:

Demonstrated that quantum kernels, implementable on current quantum hardware, yield accurate regression models for polyatomic molecule PESs.
Showcased the influence of qubit entanglement on quantum kernel properties.
Validated the generalization performance of quantum Gaussian processes through extrapolation of six-dimensional PESs.

Conclusions:

Quantum kernels are a viable tool for constructing accurate molecular PES regression models.
Bayesian optimization effectively enhances the performance of quantum kernel-based machine learning models.
Quantum Gaussian processes show promise for extrapolating complex molecular energy landscapes.