7.2k citations · 7.3k across the 2 of their papers we have counts for
6 papers · 1 filter
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…
Using models to improve optimizers for variational quantum algorithms
Kevin J. Sung, Jiahao Yao, Matthew P. Harrigan +5
Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization out…
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley +83
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algor…
Hartree-Fock on a superconducting qubit quantum computer
Frank Arute, Kunal Arya, Ryan Babbush +79
As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising direction…
Supplementary information for "Quantum supremacy using a programmable superconducting processor"
Frank Arute, Kunal Arya, Ryan Babbush +74
This is an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 201…
Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon, Michael Broughton, Jarrod R. McClean +5
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.…