Empirical performance bounds for quantum approximate optimization

DOI10.1007/s11128-021-03342-3OpenAlexW3215922063MaRDI QIDQ2099650

George Siopsis, Rebekah Herrman, James Ostrowski, Phillip C. Lotshaw, Travis S. Humble

Publication date: 24 November 2022

Published in: Quantum Information Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.06813

zbMATH Keywords

quantum algorithmsbenchmarksQAOAquantum approximate optimization

Mathematics Subject Classification ID

Quantum computation (81P68) Quantum algorithms and complexity in the theory of computing (68Q12)

Related Items (4)

An introduction to variational quantum algorithms for combinatorial optimization problems ⋮ Impact of graph structures for QAOA on maxcut ⋮ Quantum k-community detection: algorithm proposals and cross-architectural evaluation ⋮ Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm

Uses Software

• Couenne

Cites Work

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• Impact of graph structures for QAOA on maxcut
• Lower bounds on circuit depth of the quantum approximate optimization algorithm
• Branching and bounds tighteningtechniques for non-convex MINLP
• Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
• Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator
• Optimal Inapproximability Results for MAX‐CUT and Other 2‐Variable CSPs?

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