A QUBO Formulation for the Generalized LinkedIn Queens and Takuzu/Tango Game
This work provides QUBO mappings for combinatorial puzzles, which may be useful for researchers applying quantum optimization to constraint satisfaction problems, but the contribution is incremental.
The authors present QUBO formulations for generalized LinkedIn Queens, Takuzu/Tango, and related games, aiming to reduce variable count and interactions for quantum computing. They also introduce two new problem types with QUBO formulations.
In this paper, we present a QUBO formulation designed to solve a series of generalisations of the LinkedIn queens game, a version of the N-queens problem, for the Takuzu game (or Binairo), for the most recent LinkedIn game, Tango, and for its generalizations. We adapt this formulation for several particular cases of the problem, as Tents \& Trees, by trying to optimise the number of variables and interactions, improving the possibility of applying it on quantum hardware by means of Quantum Annealing or the Quantum Approximated Optimization Algorithm (QAOA). We also present two new types of problems, the Coloured Chess Piece Problem and the Max Chess Pieces Problem, with their corresponding QUBO formulations.