Evolutionary quantum game

We present a study of a dynamical quantum game. Each agent has a 'memory' of her performance over the previous m timesteps, and her strategy can evolve in time. The game exhibits distinct regimes of optimality. For small m the classical game performs better, while for intermediate m the relative performance depends on whether the source of qubits is 'corrupt'. For large m, the quantum players dramatically outperform the classical players by 'freezing' the game into high-performing attractors in which evolution ceases.