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Benchmarking Variational Quantum Eigensolvers for Quantum Chemistry

arXiv
Authors: Jiaqi Hu, Junning Li, Yanling Lin, Hanlin Long, Xu-Sheng Xu, Zhaofeng Su, Wengang Zhang, Yikang Zhu, Man-Hong Yung

Year

2022

Paper ID

6578

Status

Preprint

Abstract Read

~2 min

Abstract Words

165

Citations

N/A

Abstract

Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has become the method of choice. In the literature, there are many different variants of VQE, but it is not known which one is optimal for a given molecule. For this purpose, we perform a thorough benchmarking on more than ten different kinds of VQE ansatzes (in systems up to 30 qubits), based on their performance on the energy accuracy, runtime until convergence, and number of parameters. Our results show that the ADAPT ansatz can be used to obtain more accurate energy for small systems (below 14 qubits), but it costs much more computational resources. For larger molecules, UCCSD0 has better performance. However, all the tested ansatzes can hardly reach chemical accuracy at stretched bond lengths. Our results were obtained using MindSpore Quantum, where the codes and the benchmarking toolkit are publicly available at Gitee.

Why This Paper Matters

  • This paper contributes to the Quantum Chemistry research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • Quantum chemistry is one of the most promising applications of quantum computers in the near future.

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