activity
20042023
most citedThe Variational Quantum Eigensolver: a review of methods and best practices

1.1k citations · 6.2k across the 62 of their papers we have counts for

collaborators

62 papers

quant-ph2023★ 2 cited

Shadow-based quantum subspace algorithm for the nuclear shell model

Ruyu Yang, Tianren Wang, Bing-Nan Lu +2

In recent years, researchers have been exploring the applications of noisy intermediate-scale quantum (NISQ) computation in various fields. One important area in which quantum comp…

quant-ph2023★ 1 cited

A resource-efficient quantum-classical hybrid algorithm for energy gap evaluation

Yongdan Yang, Ying Li, Xiaosi Xu +1

Estimating the eigenvalue or energy gap of a Hamiltonian H is vital for studying quantum many-body systems. Particularly, many of the problems in quantum chemistry, condensed matte…

quant-ph2023★ 16 cited

Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

Qingyu Li, Yuhan Huang, Xiaokai Hou +3

Classification is one of the main applications of supervised learning. Recent advancement in developing quantum computers has opened a new possibility for machine learning on such…

quant-ph2023★ 7 cited

Error-Mitigated Quantum Simulation of Interacting Fermions with Trapped Ions

Wentao Chen, Shuaining Zhang, Jialiang Zhang +7

Quantum error mitigation has been extensively explored to increase the accuracy of the quantum circuits in noisy-intermediate-scale-quantum (NISQ) computation, where quantum error…

quant-ph2023★ 11 cited

Measurement-efficient quantum Krylov subspace diagonalisation

Zongkang Zhang, Anbang Wang, Xiaosi Xu +1

The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, can be much more powerful when generalised to quantum…

quant-ph2022★ 1 cited

Unbiased random circuit compiler for time-dependent Hamiltonian simulation

Xiao-Ming Zhang, Zixuan Huo, Kecheng Liu +2

Time-dependent Hamiltonian simulation (TDHS) is a critical task in quantum computing. Existing algorithms are generally biased with a small algorithmic error , and the…