PIRSA:19070079 Conference Condensed Matter

DOI10.48660/19070079

Conference/School: Machine Learning for Quantum Design

Mbend, G. (2019). The Quantum Approximate Optimization Algorithm and spin chains. Perimeter Institute. https://pirsa.org/19070079

Mbend, Glen. The Quantum Approximate Optimization Algorithm and spin chains. Perimeter Institute, Jul. 09, 2019, https://pirsa.org/19070079 

@misc{ pirsa_PIRSA:19070079,
  doi = {10.48660/19070079},
  url = {https://pirsa.org/19070079},
  author = {Mbend, Glen},
  keywords = {Condensed Matter},
  language = {en},
  title = {The Quantum Approximate Optimization Algorithm and spin chains},
  publisher = {Perimeter Institute},
  year = {2019},
  month = {jul},
  note = {PIRSA:19070079 see, \url{https://pirsa.org}}
}

Abstract

Various optimization problems that arise naturally in science are frequently solved by heuristic algorithms. Recently, multiple quantum enhanced algorithms have been proposed to speed up the optimization process, however a quantum speed up on practical problems has yet to be observed. One of the most promising candidates is the Quantum Approximate Optimization Algorithm (QAOA), introduced by Farhi et al. I will then discuss numerical and exact results we have obtained for the quantum Ising chain problem and compare the performance of the QAOA and the Quantum Annealing algorithm. I will also briefly describe the landscape that emerges from the optimization problem and how techniques borrowed from machine learning can be used to improve the optimization process.

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