1.1k citations · 6.8k across the 41 of their papers we have counts for
6 papers · 1 filter
Accurately computing electronic properties of a quantum ring
C. Neill, T. McCourt, X. Mi +89
A promising approach to study condensed-matter systems is to simulate them on an engineered quantum platform. However, achieving the accuracy needed to outperform classical methods…
Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni +4
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provide…
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…
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…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…