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Quantum Chemistry

Hybrid quantum-classical neural network for calculating ground state energies of molecules

arXiv
Authors: Rongxin Xia, Sabre Kais

Year

2019

Paper ID

39914

Status

Preprint

Abstract Read

~2 min

Abstract Words

111

Citations

N/A

Abstract

We present a hybrid quantum classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H2, LiH and BeH2. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.

Why This Paper Matters

  • This paper contributes to the Quantum Chemistry research area in the Quantum Articles archive.
  • It adds a 2019 reference point for readers tracking recent quantum research.
  • We present a hybrid quantum classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1912.06184 [2] arXiv https://arxiv.org/abs/1912.06184 [3] Publisher https://arxiv.org/abs/1912.06184

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