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Using the Quantum Approximate Optimization Algorithm (QAOA) to Solve Binary-Variable Optimization Problems
Manage episode 338237860 series 2487640
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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.
416 episodes
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Manage episode 338237860 series 2487640
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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.
416 episodes
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Similar to Software Engineering Institute (SEI) Podcast Series
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