On the complexity and verification of quantum random circuit sampling

By Adam Bouland, Bill Fefferman, Chinmay Nirkhe, Umesh V. Vazirani. Nature Physics, 2018

353 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fea8f055e6cd26f690e0e96cd099f7e3f3fb8ca1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. A. Nahum
2. Hsin-Yuan Huang
3. Matthew Fisher
4. V. Khemani
5. Peter Zoller
6. Michael J. Gullans
7. Richard Kueng
8. John Preskill

In Computational Theory and Mathematics

1. S. Vijay
2. Matthew Fisher
3. Peter Zoller
4. Richard Kueng
5. A. Nahum
6. John Preskill
7. Hsin-Yuan Huang
8. Michael J. Gullans

In Electrical and Electronic Engineering

1. S. Vijay
2. Peter Zoller
3. John Preskill
4. Steven T. Flammia

Citations in the corpus

• Random Quantum Circuits (2022, background): “Since sampling the output of a sufficiently deep, random quantum circuit is believed to be prohibitively difficult for a classical computer (124), an ensemble of random circuits has recently been implemented experimentally in an attempt to demonstrate “quantum supremacy” (125), leading to further in”
• Logical quantum processor based on reconfigurable atom arrays (2023, methodology): “Our implemented circuits are equivalent to IQP circuits 98 , which gives a theoretical basis for understanding why our circuits could be classically hard to simulate, for which we also provide numerical evidence of so-called anticoncentration 99,100 .”
• The randomized measurement toolbox (2022, background): “Variations of this idea are known as cross-entropy benchmarking 83–85 and random circuit sampling 86,87 .”