Trainability Barriers in Low-Depth QAOA Landscapes
Author:
Affiliation:
1. University of Maryland, College Park, MD, USA
2. Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3649153.3649204
Reference29 articles.
1. Quantum variational algorithms are swamped with traps
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3. Sami Boulebnane and Ashley Montanaro. 2022. Solving boolean satisfiability problems with the quantum approximate optimization algorithm. (2022). https://doi.org/10.48550/arXiv.2208.06909 arXiv:arXiv:quant-ph/2208.06909
4. Fernando G. S. L. Brandão, Mick Broughton, Edward Farhi, Sam Gutmann, and Hartmut Neven. 2018. For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances. arXiv: Quantum Physics (2018). https://api.semanticscholar.org/CorpusID:118922126
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