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publications
Evaluation of QAOA based on the approximation ratio of individual samples
Published in Quantum Science and Technology, 2022
We simulate the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives. We show that the QAOA performance varies significantly with the graph type.
Recommended citation: Jason Larkin et al 2022 Quantum Sci. Technol. 7 045014
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
11-860: Advanced Topics: Quantum Computing Lab
Spring Semester, Carnegie Mellon University, 2020
17-617: Programming Universal Gate Quantum Computers
Fall Semester, Carnegie Mellon University, 2020
11-860: Quantum Computing Lab: Self-Paced
Spring Semester, Carnegie Mellon University, 2021
17-617: Programming Universal Gate Quantum Computers
Fall Semester, Carnegie Mellon University, 2021
11-860: Quantum Computing Theory and Lab
Spring Semester, Carnegie Mellon University, 2022
17-617: Programming Universal Gate Quantum Computers
Fall Semester, Carnegie Mellon University, 2022
17-620: Quantum Circuit Mappings
Fall Semester, Carnegie Mellon University, 2022
17-659: Generative AI for Quantum Computing and Machine Learning Software Implementations
Summer Semester, Carnegie Mellon University, 2023
17-617: Programming Universal Gate Quantum Computers
Fall Semester, Carnegie Mellon University, 2023
17-620: Quantum Machine Learning
Fall Semester, Carnegie Mellon University, 2023
11-860: Quantum Computing, Cryptography, & Machine Learning Lab
Spring Semester, Carnegie Mellon University, 2024
17-628: Applied Quantum Computing
Spring Semester, Carnegie Mellon University, 2024
17-617: Programming Quantum Computers
Fall Semester, Carnegie Mellon University, 2024
17-620: Quantum Machine Learning
Fall Semester, Carnegie Mellon University, 2024
11-860: Quantum Computing, Cryptography, & Machine Learning Lab
Spring Semester, Carnegie Mellon University, 2025