Quantum Theoretic Values of Collaborative and Self-organizing Agents

When multiple agents collaborate to perform distributed operations, they can be modeled as cooperative games. Considering a network of agents work together and they can only communicate in a limited way (e.g., only to neighbor peers), the goal is to maximize the cooperation success globally, or maximize the total value and social welfare of the whole network. The type of cooperation is challenging since the game is not zero-sum. There are not any outside agents to serve as referees. The objective functions may be non-stationary and non-convex. In this paper, each agent is modeled as a content supplier or consumer. Each agent optimizes its own objective locally. We show that each agent self-organizes or converges to its "value" via the principles of quantum computing and game theories. We prove two theorems that can optimize an agent's own objective and simultaneously optimize the global social welfare of its peer network. The quantum intelligence game algorithms are unsupervised and self-organizing, where the weights expressed in quantum neural networks or transformers can be computed from a natural mechanism known as a quantum adiabatic evolution.