The rapid evolution of quantum computing has opened new frontiers in computation with exponentially faster solutions for certain problems intractable for classical systems. However, scaling while keeping low error rates, coherence times, and efficiency represents the biggest challenge to scaling quantum computers. In this chapter, we outline the synergy of AQS and AGI-driven self-optimization techniques, exploring how AGI can significantly improve the performance of quantum computing through intelligent error correction, resource allocation, and management. Autonomous quantum systems combined with AGI’s self-learning capabilities will form a paradigm shift where quantum processors no longer just execute complex quantum algorithms but also self-optimize autonomously in light of evolving computational needs to overcome the constraints of present quantum hardware. This synthesis of AGI and quantum computing promises to overcome the quantum error correction bottlenecks and manage decoherence while allowing real-time decision-making in quantum operations. This chapter further deals with the methodologies, experimental setup, and results demonstrating that AGI can be capable of quantum computing, indicating how self-optimization leads to efficiency and reliability of the quantum system. We will also cover, through some mathematical formulations and experimental metrics, how the integration of AGI will open the route to a much more self-sustaining and fault-tolerant quantum computer of the type needed to treat real problems in areas including cryptography, material science, and machine learning. This is a panoramic outlook on future computation that is being shaped with AGI-driven quantum systems.