• Corpus ID: 239049662

Diversity measure for discrete optimization: Sampling rare solutions via algorithmic quantum annealing

@inproceedings{Mohseni2021DiversityMF,
  title={Diversity measure for discrete optimization: Sampling rare solutions via algorithmic quantum annealing},
  author={Masoud Mohseni and Marek M. Rams and Sergei V. Isakov and Daniel Eppens and Susanne Pielawa and Johan Strumpfer and Sergio Boixo and Hartmut Neven},
  year={2021}
}
Sampling a diverse set of high-quality solutions for hard optimization problems is of great practical relevance in many scientific disciplines and applications, such as artificial intelligence and operations research. One of the main open problems is the lack of ergodicity, or mode collapse, for typical stochastic solvers based on Monte Carlo techniques leading to poor generalization or lack of robustness to uncertainties. Currently, there is no universal metric to quantify such performance… 

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