Robotic hand kinematic design involves combinatorial selection among multiple finger structures whose performance depends on both individual kinematic characteristics and inter-finger interactions. This study presents a potential-dexterity-based quadratic unconstrained binary optimization (QUBO) formulation for combinatorial optimization of robotic hand kinematic structures. Potential dexterity-related metrics were evaluated through kinematic analysis and encoded into a 27-variable QUBO problem incorporating individual finger performance, thumb-to-finger workspace-overlap interactions, one-hot selection constraints, and pairwise structural compatibility. Exhaustive evaluation of the design space established a reference optimum and enabled direct validation of the formulated objective. Discretization sensitivity analysis showed that the selected design remained unchanged across the tested joint-space sampling intervals and at a finer workspace voxel resolution, while sensitivity was observed under coarse voxelization. Simulated annealing consistently recovered the reference optimum at higher sampling counts, and direct execution on D-Wave quantum annealing hardware also recovered the reference optimum under all tested sampling conditions. These results demonstrate that the proposed formulation provides a common QUBO representation for constrained robotic hand kinematic structure selection and enables evaluation using exhaustive, classical annealing, and quantum annealing approaches.

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