Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets-Reference-Cited by-同舟云学术

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Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets

Published:2020-10-20 Issue:2 Volume:1 Page:
ISSN:2691-3399
Container-title:PRX Quantum
language:en
Short-container-title:PRX Quantum

Author:

Lacroix Nathan^ORCID,Hellings Christoph^ORCID,Andersen Christian Kraglund^ORCID,Di Paolo Agustin,Remm Ants^ORCID,Lazar Stefania,Krinner Sebastian^ORCID,Norris Graham J.,Gabureac Mihai,Heinsoo Johannes^ORCID,Blais Alexandre,Eichler Christopher,Wallraff Andreas

Funder

Office of the Director of National Intelligence

Intelligence Advanced Research Projects Activity

U.S. Army Research Office

National Centre of Competence in Research Quantum Science and Technology

Swiss National Science Foundation

ETH Zurich

NSERC

Canada First Research Excellence Fund

Publisher

American Physical Society (APS)

Subject

General Medicine

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PRXQuantum.1.020304/fulltext

Reference30 articles.

1. Quantum Chemistry in the Age of Quantum Computing

2. Quantum Error Correction

3. Quantum Computing in the NISQ era and beyond

4. Quantum supremacy using a programmable superconducting processor

5. Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator

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3. A review on Quantum Approximate Optimization Algorithm and its variants;Physics Reports;2024-06

4. Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction;Quantum Machine Intelligence;2024-05-24

5. Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes;New Journal of Physics;2024-05-01

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