This skill introduces methods for simulating quantum computation on classical High-Performance Computing (HPC) systems. Learners examine different quantum simulation strategies, including state-vector simulation, tensor-network approaches, and sampling-based and approximate methods, and how their computational cost scales with system size.

The focus is on understanding memory requirements, parallelisation strategies, and practical limits, rather than on simulator implementation details.

• QC1.3 Multi-Qubit Systems and the Gate Model of Quantum Computation

• Evaluate state-vector simulation in terms of computational scaling, memory requirements, and practical HPC limits.
• Assess the suitability of tensor-network simulation based on circuit structure, entanglement, and computational cost.
• Evaluate sampling-based and approximate simulation methods in terms of the trade-offs between accuracy, scalability, and resource requirements.
• Assess when sampling-based and approximate simulation methods are suitable relative to exact state-vector simulation.

• QC6.1.1 State-Vector Quantum Simulation
• QC6.1.2 Tensor-Network-Based Quantum Simulation
• QC6.1.3 Sampling-Based and Approximate Quantum Simulation