The field of quantum computing is moving towards integrating quantum principles with machine learning and social systems, enabling the modeling of uncertainty, emergence, and interdependence in complex systems. Researchers are exploring the use of quantum generative adversarial networks (QGANs) to enhance data processing and classification, as well as quantum ensemble models for learning on small data problems in healthcare and life sciences. Additionally, quantum mechanics is being combined with generative AI to simulate the emergence and evolution of social norms, revealing emergent patterns and offering insights into how society can be understood as a dynamic system to simulate and redesign. Quantum-inspired genetic optimization is also being applied to solve real-world problems, such as patient scheduling in radiation oncology. Noteworthy papers include: Quantum Ensembling Methods for Healthcare and Life Science, which demonstrates the effectiveness of quantum ensemble models in learning on small data problems. Computational Architects of Society: Quantum Machine Learning for Social Rule Genesis, which introduces a novel computational framework for simulating the emergence and evolution of social norms using quantum principles and generative AI.