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

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

FromSoftware Engineering Institute (SEI) Podcast Series


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

FromSoftware Engineering Institute (SEI) Podcast Series

ratings:
Length:
28 minutes
Released:
Aug 18, 2022
Format:
Podcast episode

Description

In this podcast from the Carnegie Mellon University Software Engineering Institute, 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.
Released:
Aug 18, 2022
Format:
Podcast episode

Titles in the series (100)

The SEI Podcast Series presents conversations in software engineering, cybersecurity, and future technologies.