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19982024
most citedSupplementary information for "Quantum supremacy using a programmable superconducting processor"

7.2k citations · 10.7k across the 66 of their papers we have counts for

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Showing 2019Show all

7 papers · 1 filter

quant-ph2019★ 7.2k cited

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…

quant-ph2019

Optimizing quantum heuristics with meta-learning

Max Wilson, Sam Stromswold, Filip Wudarski +3

Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the per…

quant-ph2019★ 68 cited

Generalized swap networks for near-term quantum computing

Bryan O'Gorman, William J. Huggins, Eleanor G. Rieffel +1

The practical use of many types of near-term quantum computers requires accounting for their limited connectivity. One way of overcoming limited connectivity is to insert swaps in…

quant-ph2019★ 11 cited

From Ansätze to Z-gates: a NASA View of Quantum Computing

Eleanor G. Rieffel, Stuart Hadfield, Tad Hogg +12

For the last few years, the NASA Quantum Artificial Intelligence Laboratory (QuAIL) has been performing research to assess the potential impact of quantum computers on challenging…

quant-ph2019★ 124 cited

Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation

Benjamin Villalonga, Dmitry Lyakh, Sergio Boixo +6

Noisy Intermediate-Scale Quantum (NISQ) computers are entering an era in which they can perform computational tasks beyond the capabilities of the most powerful classical computers…

cs.LG2019★ 27 cited

Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning

Max Wilson, Thomas Vandal, Tad Hogg +1

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a gr…