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Using the Quantum Approximate Optimization Algorithm (QAOA) to Solve Binary-Variable Optimization Problems

August 2022 Podcast
Jason Larkin, Daniel Justice

Jason Larkin and Daniel Justice, researchers in the SEI’s AI Division, discuss a paper outlining their efforts to simulate the performance of Quantum Approximate Optimization Algorithm (QAOA) for the Max-Cut problem.

I think some of the strategies we used, the methodology, is applicable regardless of what the variational algorithm is or what the actual application/problem is that is trying to be solved.

Publisher:

Software Engineering Institute

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Abstract

Jason Larkin and Daniel Justice, researchers in the SEI’s AI Division, discuss a paper outlining their efforts to simulate the performance of Quantum Approximate Optimization Algorithm (QAOA) for the Max-Cut problem and compare it with some of the best classical alternatives, for exact, approximate, and heuristic solutions.