  This paper investigates the utilization of Quantum Computing and Neuromorphic
Computing for Safe, Reliable, and Explainable Multi_Agent Reinforcement
Learning (MARL) in the context of optimal control in autonomous robotics. The
objective was to address the challenges of optimizing the behavior of
autonomous agents while ensuring safety, reliability, and explainability.
Quantum Computing techniques, including Quantum Approximate Optimization
Algorithm (QAOA), were employed to efficiently explore large solution spaces
and find approximate solutions to complex MARL problems. Neuromorphic
Computing, inspired by the architecture of the human brain, provided parallel
and distributed processing capabilities, which were leveraged to develop
intelligent and adaptive systems. The combination of these technologies held
the potential to enhance the safety, reliability, and explainability of MARL in
autonomous robotics. This research contributed to the advancement of autonomous
robotics by exploring cutting-edge technologies and their applications in
multi-agent systems. Codes and data are available.
