most citedImproving the accuracy of quantum computational chemistry using the transcorrelated method

32 citations · 32 across the 1 of their papers we have counts for

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quant-ph2020

Learning from physics experiments, with quantum computers: Applications in muon spectroscopy

Sam McArdle

Computational physics is an important tool for analysing, verifying, and -- at times -- replacing physical experiments. Nevertheless, simulating quantum systems and analysing quant…

quant-ph2020

Observation of separated dynamics of charge and spin in the Fermi-Hubbard model

Frank Arute, Kunal Arya, Ryan Babbush +96

Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity. Simulating these systems on quantum computers may avo…

quant-ph2020

Error mitigation via verified phase estimation

Thomas E. O'Brien, Stefano Polla, Nicholas C. Rubin +5

The accumulation of noise in quantum computers is the dominant issue stymieing the push of quantum algorithms beyond their classical counterparts. We do not expect to be able to af…

quant-ph202032 cited

Improving the accuracy of quantum computational chemistry using the transcorrelated method

Sam McArdle, David P. Tew

Accurately treating electron correlation in the wavefunction is a key challenge for both classical and quantum computational chemistry. Classical methods have been developed which…

quant-ph2018

Digital quantum simulation of molecular vibrations

Sam McArdle, Alex Mayorov, Xiao Shan +2

Molecular vibrations underpin important phenomena such as spectral properties, energy transfer, and molecular bonding. However, obtaining a detailed understanding of the vibrationa…

quant-ph2018

Quantum computational chemistry

Sam McArdle, Suguru Endo, Alan Aspuru-Guzik +2

One of the most promising suggested applications of quantum computing is solving classically intractable chemistry problems. This may help to answer unresolved questions about phen…