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

With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing kernel Hilbert spaces. We show that quantum kernels obtained with a fixed ansatz implementable on current quantum comput...

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Bibliographic Details
Published in:The Journal of chemical physics 2022-05, Vol.156 (18), p.184802-184802
Main Authors: Dai, J., Krems, R. V.
Format: Article
Language:English
Subjects:
Gaussian process
Hilbert space
Kernels
Optimization
Physics
Polyatomic molecules
Potential energy
Quantum computers
Quantum computing
Quantum entanglement
Qubits (quantum computing)
Regression models
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Summary:With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing kernel Hilbert spaces. We show that quantum kernels obtained with a fixed ansatz implementable on current quantum computers can be used for accurate regression models of global potential energy surfaces (PESs) for polyatomic molecules. To obtain accurate regression models, we apply Bayesian optimization to maximize marginal likelihood by varying the parameters of the quantum gates. This yields Gaussian process models with quantum kernels. We illustrate the effect of qubit entanglement in the quantum kernels and explore the generalization performance of quantum Gaussian processes by extrapolating global six-dimensional PESs in the energy domain.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0088821