# QC4.3 Implementing Variational Algorithms End-to-End

This skill introduces the practical implementation of variational quantum algorithms as hybrid quantum-classical workflows. Learners implement a simple variational algorithm, combining parameterised quantum circuits with a classical optimisation loop. Emphasis is placed on understanding the end-to-end execution pattern, including repeated circuit evaluation, expectation value estimation, and parameter updates.

The focus is on workflow comprehension and practical execution rather than mathematical derivation or performance tuning.

## Requirements

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

## Learning Outcomes

* Describe the end-to-end workflow of a variational quantum algorithm.
* Implement a parameterised quantum circuit using a quantum programming framework.
* Execute a hybrid quantum-classical loop involving circuit execution and classical optimisation.
* Compute and interpret expectation values used to guide parameter updates.
* Distinguish the practical execution workflow of a variational algorithm from that of a gate-model algorithm.