On Q-Resolution and CDCL QBF Solving

@article{Janota2016OnQA,
  title={On Q-Resolution and CDCL QBF Solving},
  author={Mikol{\'a}s Janota},
  journal={Electronic Colloquium on Computational Complexity (ECCC)},
  year={2016},
  volume={23},
  pages={43}
}
Q-resolution and its variations provide the underlying proof systems for the DPLL-based QBF solvers. While (long-distance) Q-resolution models a conflict driven clause learning (CDCL) QBF solver, it is not known whether the inverse is also true. This paper provides a negative answer to this question. This contrasts with SAT solving, where CDCL solvers have been shown to simulate resolution. 

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