Framing Informational Advantage in Robotic Multiagent Cyberphysical Security Using Quantum Reinforcement Learning

Quantum computing is anticipated to impact robotic multi-agent military systems. However, computational advantage remains unlikely at military scales in the near term, as it requires fault-tolerant quantum hardware. Instead, this paper advances the concept of informational advantage: quantum computing models may obtain information that classical models cannot feasibly access. To address this, we model, using a synalgebraic framework, a concrete military scenario involving jamming-spoofing attacks against robotic multi-agents. A quantum reinforcement learning algorithm is introduced to exploit adversarial multi-agent interactions. Preliminary results obtained from a gamified scenario indicate the plausibility of informational advantage in settings where classical approaches fail due to informational complexity rather than computational limitations. It concludes by outlining how data from realistic classical simulators and quantum hardware can be considered too.