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Amazon Braket's integration with AWS Batch enables scalable hybrid workflows for advanced algorithms like QC-AFQMC, effectively decoupling quantum sampling from resource-intensive classical post-processing. This architecture addresses the growing demand for high-performance computing resources required to handle large datasets generated after quantum measurements. By orchestrating these sequential steps via AWS Step Functions, the solution streamlines complex quantum-classical interactions essential for practical quantum advantage applications.
Amazon Braket integrates with AWS Batch for scalable workflows
Running scalable hybrid quantum-classical workflows is possible by integrating Amazon Braket with AWS Batch into a scalable compute pipeline orchestrated with AWS Step Functions. This solution can be adapted to algorithms such as SQD that share a similar workflow pattern.
Algorithms like Quantum-Classical Auxiliary-Field Quantum Monte Carlo (QC-AFQMC) and Sample-Based Quantum Diagonalization (SQD) are driving a need for high-performance computing resources after quantum measurements are collected, a shift in how quantum and classical computers interact. These algorithms collect measurement samples from a quantum processing unit and then feed them into classical post-processing that can demand significant HPC resources. Benchen Huang, Sebastian Stern, and Tyler Takeshita explain that Amazon Braket now integrates with AWS Batch to orchestrate these scalable hybrid workflows, creating a compute pipeline for algorithms that decouple quantum…
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