Introducing Quantum Variational Circuit for Efficient Management of Common Pool Resources

Common Pool Resources (CPRs) pose a challenge to find balance between individual self-interest and collective cooperation. The current state of art tries to solve the problem using classical Reinforcement Learning techniques. However, due to exponential search space, the training time of these algorithms is very high. On the other hand, quantum computing offers parallel processing that makes it suitable for such complex scenarios. The purpose of this study is to explore Quantum Reinforcement Learning to solve the CPR challenge. We introduced a Variational Quantum Algorithm for attaining quantum speedup in resolving CPR dilemmas and developing cooperative behaviors among agents. The proposed method shows significantly better performance then classical reinforcement learning algorithms in terms of training time and training parameters. This study offers valuable insights into harnessing the quantum advantage in real-life multi-agent scenarios, including cooperative robotics, peer-to-peer networks, and traffic optimization.