# QC6.2 Hybrid Quantum–Classical Workflows for High-Performance Computing

This skill introduces the orchestration and integration of quantum workloads into classical High-Performance Computing (HPC) environments. Learners examine hybrid workflows in which classical computation controls, schedules, and post-processes quantum subroutines. Emphasis is placed on the practical challenges that make quantum workflows distinct from typical HPC jobs, including short-duration quantum executions, frequent classical–quantum interactions, transpilation overhead, probabilistic measurement outcomes and, in error-corrected workflows, classical tasks such as syndrome decoding.

The skill focuses on workflow design, scheduling considerations, and resource management for hybrid quantum–classical pipelines.

## Requirements

* [[skill-tree:qc:4:1:b]]
* [[skill-tree:qc:5:2:b]]
* [[skill-tree:qc:5:3:b]]
* [[skill-tree:qc:6:1:b]]

## Learning Outcomes

* Explain how hybrid quantum–classical workflows differ from standard HPC workflows, emphasizing short-duration quantum executions and frequent interaction with classical control.  
* Describe the impact of quantum transpilation and compilation on workflow timing and scheduling.  
* Explain how probabilistic measurement outcomes influence iterative or hybrid algorithm execution, including repeated evaluations.  
* Describe the role of on-the-fly classical computations for error mitigation and syndrome decoding in error-corrected workflows.  
* Analyse resource management challenges in hybrid workflows, including latency, throughput, and co-location of quantum and classical resources.
* Explain architectural considerations for orchestrating hybrid quantum–HPC pipelines, including batch submission, queueing, and feedback loops.  
* Evaluate practical trade-offs when embedding quantum subroutines in HPC pipelines, such as job overhead versus algorithmic gain.