The field of art uses the principles of quantum physics to perform far more rapid computations than its conventional counterparts, hence called quantum computing. Quantum computing is a new type of computation that contemplates an uncertain and physically unpredictable reality and is underpinned by quantum mechanics. It’s going to be a great deal discussed in this chapter on the transformative potential of quantum computing at the nexus of artificial intelligence: the history and prospects of artificial general intelligence, or AGI. It talks about quantum computing, which uses ideas from quantum mechanics to create quantum bits, or Qubits, that may exist in several states simultaneously, giving rise to computational power significantly greater than that of conventional computers. It is possible for quantum supremacy because it solves complicated problems tenfold quicker than their classical equivalents. Some of the models that are discussed in the article in relation to quantum machine learning include Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Generative Adversarial Networks (QGANs). Such models extend the possibilities of quantum mechanics beyond what is perceived to be capable with artificial intelligence, bringing an extensive improvement in data processing, optimization, and generation. Thus, it makes it feasible to ensure data privacy when using quantum computing and federated learning, which are possible for cooperative training of models across dispersed devices. One of the critical points covered in the book relates to guaranteeing clarity and confidence behind judgments made by AI in quantum systems through Explainable AI or XAI. Other methods of making quantum AI models interpretable are Quantum SHAP and Quantum Layer-wise Relevance Propagation (Q-LRP). This chapter outlines, in general, how quantum computing can transform artificial intelligence toward achieving unprecedented levels of processing power, efficiency, and transparency toward achieving AGI.