# QC6 Integration of Quantum and High-Performance Computing

This skill explores how quantum computing can be integrated into High-Performance Computing (HPC) environments and workflows.
Learners examine hybrid quantum–classical approaches in which classical HPC resources prepare circuits, submit them for execution on simulators or quantum processors, and post-process measurement results.
The skill focuses on integration patterns and constraints that matter in HPC settings, including co-locating quantum workloads with classical pre-/post-processing, managing latency and throughput, and assessing end-to-end time-to-solution.
Representative examples (e.g., chemistry/materials, optimisation, and machine learning) are used to illustrate both opportunities and practical limitations such as noise, limited throughput, and the impact of sampling on runtime.

## Requirements

External:
* Basic linear algebra

Internal:
* [[skill-tree:qc:1:3:b]]

## Learning Outcomes

* Evaluate classical quantum simulation approaches in terms of their suitability, scalability, accuracy, and HPC resource requirements.
* Analyse the orchestration of hybrid quantum–HPC workflows and the coordination of quantum and classical resources.
* Evaluate the performance trade-offs of hybrid quantum–HPC workflows, including latency, throughput, and end-to-end time-to-solution.
* Analyse how quantum subroutines are integrated into larger HPC workflows across representative application domains.
* Assess the effectiveness, potential benefits, and practical limitations of quantum–HPC workflows relative to appropriate classical methods.


## Subskills

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