The Quantum Approximate Optimization Algorithm and spin chains

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

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.