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Kevin Sung

Researcher at University of California, Los Angeles

Publications -  25
Citations -  6517

Kevin Sung is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Quantum computer & Qubit. The author has an hindex of 13, co-authored 23 publications receiving 3229 citations. Previous affiliations of Kevin Sung include Veterans Health Administration & Google.

Papers
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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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Hartree-Fock on a superconducting qubit quantum computer

TL;DR: Several quantum simulations of chemistry with up to one dozen qubits are performed, including modeling the isomerization mechanism of diazene, and error-mitigation strategies based on N-representability that dramatically improve the effective fidelity of the experiments are demonstrated.
Journal ArticleDOI

Hartree-Fock on a superconducting qubit quantum computer

TL;DR: In this paper, a series of quantum simulations of chemistry were performed, the largest of which involved a dozen qubits, 78 two-qubit gates, and 114 one qubit gates.
Posted Content

Learning to learn with quantum neural networks via classical neural networks

TL;DR: This work trains classical recurrent neural networks to assist in the quantum learning process, also know as meta-learning, to rapidly find approximate optima in the parameter landscape for several classes of quantum variational algorithms.
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Quantum algorithms to simulate many-body physics of correlated fermions

TL;DR: In this article, the authors develop quantum algorithms that run on two-dimensional qubit lattices with nearest-neighbor interactions, to simulate strongly correlated fermions, avoiding the parity problem in mapping fermionic operators to qubit operators, with hardly any overhead.