Hyperparameter optimization for quantum machine learning with Amazon Braket

This article discusses a cost-effective approach for developing and optimizing hybrid quantum-classical machine learning algorithms using Amazon Braket. It focuses on hyperparameter optimization (HPO) for quantum image classification.

Specifically, the article covers:

• A three-step development cycle: ideation in Amazon Braket notebooks, scaling with Hybrid Jobs and HPO, and verification on Braket QPUs
• Using HPO and simulators to find optimal hyperparameters before running on real QPUs
• Details on the quantum image classification task for distinguishing bees and ants
• Simulation of noise models and benchmarking performance on different simulators and devices
• Cost estimation and optimization for running QPU experiments
• Conclusion on the cost-effective QML development approach using Amazon Braket