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EVENT DETAILS |
Our next meetup will be from 6-8pm on Wednesday, February 26, 2020 at Microsoft between 41st & 42nd on 8th Ave. Please use the 8th Ave entrance.
Our speaker will be Beatrice Nash from Harvard.
Abstract
QAOA (Quantum Approximate Optimization Algorithm), VQE (Variational Quantum Eigensolver) & other variational-type quantum approximation algorithms are promising applications for noisy quantum devices, especially due to their adaptability to a variety of systems & optimization problems & relative robustness to noise compared to other quantum algorithms. These algorithms have a wide range of applications, a few of which include combinatorics, machine learning, cryptography, & chemistry, & hence are an active area of research in the field of quantum computation. However, there is still much to be done to improve the projected costs of these algorithms in order to speed up time to converge to sufficiently good solutions & understand for which problems & instances these algorithms work better or worse. In this talk, I will discuss how to analyze the complexity, performance, & sensitivity to noise of quantum approximation algorithms, as well as general & device- & problem-specific optimization techniques to improve their efficiency & result quality. I will discuss the algorithms both generally & applied to specific problems, including Max-Cut & Max-Independent Set for QAOA & the Fermi-Hubbard model for VQE. This talk will provide a comprehensive overview of the state of the art optimization techniques for quantum approximation algorithms & their future outlook.
Beatrice Nash is a graduate student in Computer Science at Harvard University with a Bachelors degree from MIT in Physics & Mathematics. Her research focuses on quantum algorithms, information & complexity.
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