Streamline machine learning workflows from local development to scalable experiments by using SageMaker AI and Hydra
This blog post explores a cost-effective development cycle for training and evaluating hybrid quantum-classical algorithms with hyperparameter optimization (HPO) on Amazon Braket. It addresses challenges in ensuring reproducibility and efficient resource utilization. The post details a three-step process: ideation in Braket notebooks, scaling with Hybrid Jobs for HPO, and verification on QPUs. It uses a variational quantum algorithm for image classification, demonstrating techniques for data reduction, model training, and performance gauging with simulators and real quantum devices, providing insights for practical QML projects.