Explore a comprehensive 1-hour 19-minute presentation that revisits and evaluates IBM Qiskit and Xanadu PennyLane QML/QiML demos. Learn how to optimize quantum machine learning models by applying a new rubric derived from previous experiments. Discover techniques for improving runtimes, reducing losses, and increasing accuracies through modifications to quantum simulators, circuits, and model optimizers. Gain insights into the fundamental quantum computational building blocks provided by Qiskit tutorials and the Quantum Differentiable programming capabilities of PennyLane. Access Python notebooks and additional resources to enhance your understanding of quantum machine learning applications in fields such as medicine. Review the first set of R&D notebooks and seminars available on GitHub for both Qiskit and PennyLane, and explore references for further learning.