Training Loop

A **training loop** in quantum computing is the iterative process used to optimize parameters in a hybrid quantum-classical algorithm, such as a variational quantum circuit. The quantum computer evaluates a cost function by running the circuit and measuring outputs, while a classical optimizer updates the circuit parameters to minimize (or maximize) that cost. This loop repeats until the model converges to an optimal or near-optimal solution.