Work

Machine Learning of Noise-Resilient Quantum Circuits

Year: 2021

Type: article

Abstract: The impact of hardware noise on quantum circuits is circumvented using a machine learning strategy that outputs the best circuit design for optimizing the performance of a given algorithmic task under the noisy environment.

Source: PRX Quantum

DOI: 10.1103/prxquantum.2.010324

Authors: Lukasz Cincio, Kenneth Rudinger, Mohan Sarovar, Patrick J. Coles

Institutions: Los Alamos National Laboratory, Sandia National Laboratories, Sandia National Laboratories California

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,450

Funders: U.S. Department of Energy, Office of Science, National Nuclear Security Administration, Advanced Scientific Computing Research, Sandia National Laboratories, Los Alamos National Laboratory

Awards: G5815161907, G7254900899, G855168022

OpenAlex ID: W3038966512

FWCI: 7.723

Cites: 32

Cited by: 127

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