The field of quantum computing is presently in its nascent stage, marked by the prevalent use of ad-hoc and manual-design approaches in quantum circuit design. This methodology, reminiscent of the early days of classical integrated circuits, presents a challenge to the evolution of practical quantum computing. In response to this challenge, there is a growing imperative for the development of a quantum analog to EDA --- Quantum Design Automation (QDA). This quantum-centric EDA aims to provide a systematic and automated framework for the design of quantum circuits, marking a crucial step towards overcoming the current impediments in the path to practical quantum computing. This talk undertakes a comprehensive comparison between classical and quantum computing design stacks, elucidating the intricacies introduced by qubit properties. Addressing the impact of unstable noise becomes central, providing insights into the necessities, obstacles, and opportunities in QDA. The talk outlines automated optimizations at various layers to tailor circuits to current noise, ultimately enhancing runtime fidelity. Finally, preliminary results employing quantum learning for real-world applications are presented, offering a glimpse into