Quantum annealing is an algorithm to solve combinatorial optimization problems by encoding the solutions to the problems in the ground state of an Ising Hamiltonian. This technique suffers failure in certain scenarios, for example when the energy gaps in the instantaneous spectrum are small. In this talk we will see how we can navigate this problem of having small energy gaps, by introducing a counter-diabatic operator and/or by optimizing the quantum annealing schedules. I will discuss how classical algorithms such as genetic algorithms and deep learning can be used to address this challenge effectively.
Zoom Meeting URL: https://stockholmuniversity.zoom.us/j/69740734375