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The $p$-stage Quantum Approximate Optimization Algorithm (QAOA$_p$) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum (NISQ) devices, but its theoretical behavior is not well understood beyond $p=1$.
Classical and quantum bounded depth approximation algorithms
M. B. Hastings · 1905
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Quantum annealing: a journey through digitalization, control, and hybrid quantum variational schemes
G. B. Mbeng, R. Fazio, and G. Santoro · 1906
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What do qaoa energies reveal about graphs?
M. Szegedy · 1912
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A note on bipartite subgraphs of triangle-free graphs
J. B. Shearer · 1992
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Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
M. X. Goemans and D. P. Williamson · 1995
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On the power of unique 2-prover 1-round games
S. Khot · 2002
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IPython: a system for interactive scientific computing
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J. Wurtz and P. J. Love · 2010
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The theory of variational hybrid quantum-classical algorithms
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Quantum approximate optimization algorithm for maxcut: A fermionic view
Z. Wang, S. Hadfield, Z. Jiang, and E. G. Rieffel · 2018
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
P. Virtanen, R. Gommers, T. E. Oliphant, et al · 2020
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Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices
L. Zhou, S.-T. Wang, S. Choi, et al · 2020
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A quantum approximate optimization algorithm applied to a bounded occurrence constraint problem
E. Farhi, J. Goldstone, and S. Gutmann
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Large cuts with local algorithms on triangle-free graphs
J. Hirvonen, J. Rybicki, S. Schmid, and J. Suomela
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