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.

External: * Basic linear algebra

Internal: * QC1.3 Multi-Qubit Systems and the Gate Model of Quantum Computation

• 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.

• QC6.1 Quantum Simulation on Classical High-Performance Computing Systems
• QC6.2 Hybrid Quantum–Classical Workflows for High-Performance Computing
• QC6.3 Applications and Use Cases for Quantum Workflows in High-Performance Computing