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Eleanor Rieffel

Researcher at Ames Research Center

Publications -  184
Citations -  12742

Eleanor Rieffel is an academic researcher from Ames Research Center. The author has contributed to research in topics: Quantum computer & Quantum algorithm. The author has an hindex of 36, co-authored 168 publications receiving 8942 citations. Previous affiliations of Eleanor Rieffel include Fuji Xerox & FX Palo Alto Laboratory.

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Supplementary information for "Quantum supremacy using a programmable superconducting processor"

TL;DR: In this paper, an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 2019 issue of Nature, is presented.
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Quantum supremacy using a programmable superconducting processor

Frank Arute, +85 more
- 24 Oct 2019 - 
TL;DR: Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
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From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz.

TL;DR: The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter.
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An introduction to quantum computing for non-physicists

TL;DR: Basic principles of quantum mechanics are introduced to explain where the power of quantum computers comes from and why it is difficult to harness and various approaches to exploiting the powerof quantum parallelism are explained.
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From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz

TL;DR: The quantum alternating operator ansatz (QOANSatz) as discussed by the authors is a generalization of the original quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian.