cooperation [Miakisz et al. (2006)] and coordination games [Huberman & Hogg 
(2003)], simulating economic systems [Chen et al. (2002)] and human behavior 
[Mendes (2005)]. It is interesting that in some cases the quantum solution can be derived as an extension to the classical one [Huberman & Hogg (2003)], thus enabling the optional use of quantum entanglement as an extra resource. Even more interesting is the fact that the quantum solution to these games models the human behavior much more accurately than the classical one. In fact, it seems that human player strategies in these types of games deviate significantly from the theoretical Nash equilibrium that classic game theory expects. This discrepancy (i.e. the seemingly irrational human behavior) is attributed to an emotional response of the human player. Quantum solutions such the one for the *Quantum Ultimatum Game*
[Mendes (2005)] seem to model efficiently these discrepancies and propose a better model for the human behavior in such situations. 

The last comment on modeling human behavior seems to lead to the philosophical question of whether the human brain performs some kind of quantum computation [Miakisz et al. (2006)] or not [Penrose (1997)], a question that has been used to argue against hard-AI in the past. Although there are not sufficient data to answer such a question, one could argue that with QC the idea of hard-AI seems a little closer to implementation, since there is some evidence that QC is stronger than classical Turing computation. Indeed, [Calude & Pavlov (2002)] have proved that QC is theoretically capable of computing incomputable functions. Despite Feynman's argument that QC is not able to exceed the so-called Turing's barrier and solve an undecidable problem, Calude & Pavlov have used QC to solve an equivalent to the famous Halting Problem, the most well-known undecidable problem in computer science. Although it still has to be seen whether this approach is practically feasible, this is a great theoretical breakthrough that promises to change the computational capabilities of our machines. Eventually, QC's effectiveness may eventually make us reconsider what an AI problem is. For example chess playing is traditionally considered an AI problem. That is so due to the high computational cost of the brute-force algorithmic approach in the classical computational 'world'. But in the quantum domain the same problem is not so hard. Given the proper hardware, a quantum algorithmic process would be able to solve it in acceptable time. So does chess playing remain an AI problem? Only time will tell whether QC will force us to redefine the domain of AI or whether it will be eventually considered yet another weapon in the AI arsenal, like neural networks or genetic algorithms. 

## 5. Conclusion

This paper attempted to present the basics of QC to readers already familiar with AI, explaining QC's potential application to traditional AI problems and methods through 