This skill introduces tensor-network simulation methods for classical High-Performance Computing systems. Learners examine how quantum states and circuits can be represented as interconnected tensors to reduce memory and computational requirements for certain classes of quantum problems.

Emphasis is placed on understanding the role of entanglement, contraction order, and network topology in determining computational efficiency, as well as the types of circuits where tensor-network simulation is most effective.

• QC1.3 Multi-Qubit Systems and the Gate Model of Quantum Computation
• QC6.1.1 State-Vector Quantum Simulation

• Explain the basic concept of representing quantum states and operations as tensor networks.
• Describe how tensor-network representations can reduce memory and computational requirements for suitable quantum circuits.
• Explain how entanglement and network topology affect the effectiveness of tensor-network simulation.
• Describe how contraction order influences computational cost.
• Identify classes of quantum circuits where tensor-network simulation is advantageous.