The recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations is reviewed here. The concept is introduced in successive steps through studying the mapping of such computationally hard problems to classical spin-glass problems, quantum spin-glass problems arising with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.
Information scrambling may become enhanced for systems with many metastable configurations, such as glasses, when the complex dynamics samples the multitude of configurations. The authors study here the scrambling in chains of quantum dots, modeled via two paradigmatic spin glass models -- Sachdev-Ye and
p-spin glass -- and show that the butterfly velocity characterizing scrambling is enhanced above the glass transition where the dynamics is maximally complex. However, the evolution of glassy complexity with quantum and thermal fluctuations in the two spin glasses is very different.