Running scalable hybrid quantum-classical workflows by integrating Amazon Braket with AWS Batch

This article demonstrates how to orchestrate scalable hybrid quantum-classical workflows by integrating Amazon Braket with AWS Batch and AWS Step Functions.

• Use Amazon Braket Hybrid Jobs for quantum sampling with shadow tomography to prepare trial states
• Run embarrassingly parallel classical post-processing on AWS Batch to evolve thousands of Monte Carlo walkers independently
• Use AWS Lambda for final energy averaging from per-walker results stored in S3
• AWS Step Functions orchestrates the three-stage workflow as a state machine with automatic sequencing and error handling
• Example demonstrates QC-AFQMC algorithm for ground-state energy estimation of hydrogen molecules
• Cloud-native pipeline allows application users to define, deploy, and version-control the entire workflow

This architecture enables efficient execution of hybrid quantum-classical algorithms that decouple quantum and classical subroutines into sequential steps, with independent resource scaling and built-in workflow management.