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Xiao Mi

Researcher at Google

Publications -  82
Citations -  12581

Xiao Mi is an academic researcher from Google. The author has contributed to research in topics: Qubit & Quantum computer. The author has an hindex of 26, co-authored 68 publications receiving 7709 citations. Previous affiliations of Xiao Mi include Princeton University & Cornell University.

Papers
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Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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

A coherent spin–photon interface in silicon

TL;DR: Strong coupling between a single spin in silicon and a single microwave-frequency photon, with spin–photon coupling rates of more than 10 megahertz is demonstrated, which opens up a direct path to entangling single spins using microwave- frequencies.
Journal ArticleDOI

Quantum approximate optimization of non-planar graph problems on a planar superconducting processor

Matthew P. Harrigan, +95 more
- 04 Feb 2021 - 
TL;DR: The application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) is demonstrated and an approximation ratio is obtained that is independent of problem size and for the first time, that performance increases with circuit depth.